ALBERT

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Description: In order to meet the telecommunication requirements for the management of the first-response phase of an emergency situation, SALICE project activities have been focused on two main research areas: achieving global coverage of the emergency area and providing the rescuers with advanced NAV/COM capable devices.

Description: Future deployable air surveillance and air defence may no longer use a variety of different radar systems, optimised for specific tasks, but rather employ multifunction radars, which are able to meet the requirements of multiple military scenarios and challenges. Such deployable radar sensors should utilise all technical and technological capabilities and be available in the time frame of 15 to 20 years from now.

Description: Front cover: The space observation radar TIRA (Tracking and Imaging Radar) located at the Fraunhofer Institute for High Frequency Physics and Radar Techniques FHR in Wachtberg, Germany, is pictured on our front cover this issue. The authors of an article in this issue are from Fraunhofer FHR and provided the cover photograph. Please enjoy "Deployable Multiband Passive/Active Radar for Air Defense (DMPAR)," by H. Kuschel, J. Heckenbach, and J. Schell on pages 37 through 43. Back Cover: PLANS 2014 Call for Abstracts. Abstracts are due November 1, 2013. The Position Location and Navigation Symposium (PLANS) will be held May 5-8, 2014 in Monterey, California. Please plan to participate. For more information visit www.plansconference.org.

Description: The CE-1 satellite was successfully launched on October 24, 2007, and was guided to crash-land on the Moon on March 1, 2009. The FPGA of the CCD stereo camera operated more than 1 year without faults, exceeding the expected life span by about 4 months. The first lunar image from CE-1 was obtained on November 20 and November 21, 2007, and was released on November 26, 2007 (Figure 14). A three-dimensional image was acquired by processing at the same time and released on November 26, 2007 (Figure 15). The raw data obtained by the CCD stereo camera included 508 orbit images between the latitudes of 70??N and 70??S and 589 polar orbit images. Many scientific results have been obtained by analyzing these data [26], especially the ??global lunar image from the first Chinese lunar exploration mission?? in [27]. The results of the onboard flight validate that the FPGA design of the CCD stereo camera is reasonable and reliable and has achieved the mission requirements.

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Description: One of the best duties of the Editor-In-Chief of the IEEE Transactions on Aerospace and Electronic Systems (IEEE TAES) is announcing the winner of the M Barry Carlton award, which was established to acknowledge the best paper published in each calendar year. As stated in the IEEE AESS website, the award was established in 1957 in memory of a man who dedicated his life to promoting the reliability of communications equipment. The basis for the award is demonstration of an important original advancement in the state of the art in an AES technology.

Description: The northern fringe of sub-Saharan Africa is a region that is considered to be particularly vulnerable to climate variability and change, and it is a location in which food security remains a major challenge. To address these issues, it is essential to develop global data sets of the geographic distribution of agricultural land use. The objectives of this study were to test an original data mining approach for classifying and mapping the cropped land in West Africa using coarse-resolution imagery and to compare the classification results with those obtained from a classic ISODATA approach. The data mining approach is able to handle large volumes of data and is based on different descriptors (65) of the land use, including the spatial and temporal satellite-derived metrics of 12 MODIS NDVI 16-day composite images and the static attributes taken from field surveys. The classic ISODATA method showed that 68.3% of pixels from a SPOT reference map were correctly classified in three validation sites versus 57.8% for the data mining approach. Validation by field observations showed equivalent results for both methods with an F-score of 0.72. The results of this study demonstrated the relevance of the use of data-mining tools for large-area monitoring.

Description: A general framework for processing high and very-high resolution imagery in support of a Global Human Settlement Layer (GHSL) is presented together with a discussion on the results of the first operational test of the production workflow. The test involved the mapping of 24.3 million km² of the Earth surface spread in four continents, corresponding to an estimated population of 1.3 billion people in 2010. The resolution of the input image data ranges from 0.5 to 10 meters, collected by a heterogeneous set of platforms including satellite SPOT (2 and 5), CBERS 2B, RapidEye (2 and 4), WorldView (1 and 2), GeoEye 1, QuickBird 2, Ikonos 2, and airborne sensors. Several imaging modes were tested including panchromatic, multispectral and pan-sharpened images. A new fully automatic image information extraction, generalization and mosaic workflow is presented that is based on multiscale textural and morphological image features extraction. New image feature compression and optimization are introduced, together with new learning and classification techniques allowing for the processing of HR/VHR image data using low-resolution thematic layers as reference. A new systematic approach for quality control and validation allowing global spatial and thematic consistency checking is proposed and applied. The quality of the results are discussed by sensor, band, resolution, and eco-regions. Critical points, lessons learned and next steps are highlighted.

Description: This paper aims at introducing a fast and efficient approach able to extract human settlement extents using ASAR Wide Swath Mode data. The proposed approach exploits the spatial features characterizing human settlements in SAR data at a spatial resolution around 100 m, i.e., long term coherence and large backscattered power values. The joint use of multi-temporal filtering and averaging and the homogeneously high SAR return from built-up structures is the key to extract quickly and robustly human settlement extents. Although prone to commission errors in mountainous areas, the procedure proposed in this paper proved to be able to extract consistently more accurate results than existing global data sets including Globcover 2009. This was assessed by running a series of tests in different geographical areas and comparing the new and the existing products with independently extracted “urban” and “non-urban” points. The results show that ASAR data have no fewer potential than optical ones for global mapping of human settlements. Properly processed, instead, SAR data are able to provide an effective solution to the need of a global map of human settlement, useful for risk computations, climate change model inputs and population mapping, among other applications.

Description: A new stereoscopic road network extraction framework based on the decision-level fusion of optical and Synthetic Aperture Radar (SAR) imagery is proposed in this paper. Three steps are included in this framework: 1) road segment extraction and structure optimization through SAR imagery, 2) road segment extraction and stereoscopic information collection through optical imagery, and 3) fusion of the SAR result with the optical image and the stereoscopic information. In this study, our new road network grouping algorithm called road network grouping based on the multi-scale geometric analysis of detector Response is used, with the improved footprint method, and the stereoscopic inversion algorithm. The most important finding of our work lies in the fusion step, by which a stereoscopic road network can be acquired after going through the three aforementioned processes and by fusing the stereoscopic information obtained from optical imagery and road network extracted from SAR imagery. Our algorithm is tested on the real TerraSAR-X and QuickBird data.

Description: The task of enhancing the perception of a scene by combining information captured from different image sensors is usually known as multisensor image fusion. This paper presents an area-based image fusion algorithm to merge SAR (Synthetic Aperture Radar) and optical images. The co-registration of the two images is first conducted using the proposed registration method prior to image fusion. Segmentation into active and inactive areas is then performed on the SAR texture image for selective injection of the SAR image into the panchromatic (PAN) image. An integrated image based on these two images is generated by the novel area-based fusion scheme, which imposes different fusion rules for each segmented area. Finally, this image is fused into a multispectral (MS) image through the hybrid pansharpening method proposed in previous research. Experimental results demonstrate that the proposed method shows better performance than other fusion algorithms and has the potential to be applied to the multisensor fusion of SAR and optical images.

Description: This paper designed a non-target multi-point calibration method for the quality enhancement of large-area satellite images by using local aerial images. Satellite images are more sensitive to atmospheric effects compared with aerial images. Atmospheric effects on aerial images are even negligible in fine weather. Given that aerial remote sensing has high spatial resolution and geometric fidelity, more spatial details can be recorded in aerial images. However, the scan bandwidth of aerial images is limited compared with that of satellite images. Thus, taking high-quality aerial images of a neighborhood as reference can provide prior knowledge for point spread function (PSF) estimation and for the quality enhancement of large-area satellite images. The least square method and interpolation are used for the PSF estimation of spatial variation, and then total variation minimization is used for recovery. The results show that the designed method can effectively enhance the quality of large-area satellite images.

Description: Large-area mapping is of great valuable in microwave remote sensing, but it is a contradiction between swath width and azimuth resolution due to the minimum antenna area constraint. In this paper, we consider a specific wide-area mapping technique with high-altitude high-speed platform, where range ambiguity suppression is a technical challenge. To resolve this problem, we present a multiple-input multiple-output (MIMO) synthetic aperture radar (SAR) with multiple antennas placed in the cross-track direction. The increased degrees-of-freedom (DOFs) provide a potential to resolve possible range and/or azimuth ambiguities. After formalizing the system scheme and signal model, an iterative matched filtering algorithm is presented, which can efficiently suppress the cross-correlation interferences in the multichannel data separation. Furthermore, a range-Doppler based image formation algorithm is derived. The MIMO SAR system performance is evaluated by the range-ambiguity-to-signal ratio (RASR) performance. Numerical simulation results validate the effectiveness of the proposed MIMO SAR in high-altitude high-speed platform SAR for large-area remote sensing.

Description: The exploitation of X-band imagery is currently being used in the field of agriculture for the discrimination of crop types and is also recently addressed for challenging tasks such as surface roughness and soil moisture retrieval. We investigated the potential of multi-temporal COSMO-SkyMed data for the monitoring of Natura 2000 habitats in alpine region. Short revisit time of currently available VHR synthetic aperture radar instruments (like TerraSAR-X, COSMO-SkyMed etc.) and their all-weather (day/night) image acquisition capability provides an additional advantage of continuous monitoring of nature conservation sites in particular which are small in size. Preliminary analysis of VV and VH signals indicates a predominant effect of attenuation with respect to the volume contribution as expected in case of X-band for the reduced penetration capabilities in the canopy. Significant changes in the VV signals are mainly ascribed to grazing/mowing event which reduces the canopy layer and then the signal attenuation. The final results are in agreement with the ancillary data (Normalized Vegetation Index (NDVI)) and management rule record for Natura 2000 habitats present in the alpine region.

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Description: Co-registration of 2-D images and 3-D lidar points in a common area is an important task in data fusion as well as other applications. As the information acquired by image and lidar systems are not the same, the registration of heterogeneous sensors is a challenging issue. The objective of this study is to perform the co-registration of 2-D images and 3-D lidar points using relative and absolute orientations. The proposed method performs image matching between stereo images for relative orientation modeling and generates a matched 3-D surface model. Then, an automatic least squares 3-D surface matching is applied between matched 3-D surface model and lidar 3-D points. Finally, the precise object-to-image transformation and orthoimages can be generated via relative and absolute transformation. The test data include WorldView-2 image, QuickBird image and lidar data. The experimental results indicate that the relative orientation may reach subpixel accuracy while the absolute orientation may reach 1 pixel accuracy. Moreover, the geometric consistency between orthoimages is better than 0.5 m on the ground.

Description: In the last three decades, substantial advancements have been made in understanding the global carbon cycle. Some of these advancements involve using the fraction of absorbed photosynthetically active radiation (fAPAR) by an entire canopy (fAPAR $_{rm canopy}$ ) and/or the Normalized Difference Vegetation Index (NDVI) in modeling studies. In spite of these advancements, large uncertainties still remain. Zhang (Remote Sens. Environ., 2005) [1] tried to mitigate some of these uncertainties with the concept of using fAPAR that is restricted to the foliage chlorophyll (fAPAR $_{rm chl}$ ) instead of the entire canopy. In this current study, we calculated fAPAR $_{rm canopy}$ , fAPAR $_{rm chl}$ , and foliage non-chlorophyll fAPAR (fAPAR $_{rm nonmathchar "702D chl}$ ) for the Harvard Forest using a radiative transfer model and multi-temporal Earth Observing One (EO-1) Hyperion satellite images. The canopy-level proportions of foliar chlorophyll and non-chlorophyll absorption were determined at different seasons (spring, summer, autumn) in an effort to demonstrate temporal variations of three plant functional types: deciduous forest, coniferous forest, and grass. Comparisons were made for NDVI versus fAPAR $_{rm canopy}$ and for the Enhanced Vegetation Index (EVI) versus fAPAR $_{rm chl}$ . In addition, EO-1 Hyperion images were utilized to simulate these new fAPAR $_{rm canopy}$ , fAPAR

Description: Presents the table of contents for this issue of the magazine.

Description: For more than 10 years, mass memories for space applications have taken the DRAM technology family as conventional semiconductor storage technology. DRAM technology is very fast, reliable, and provides a very high data rate, but needs power back up to retain data and tends to become very inefficient (on a watt/bit scale) with increasing capacity. This is why flash technology has been considered for replacing the well-established DRAMs. NAND flash is currently the most suitable solution for nonvolatile storage in embedded applications and it is gaining access to in safety critical applications, thanks to their high storage density, low power, low cost, and high data throughput. However, NAND flash research and literature in the safety-critical environment is not as established as in the commercial applications. As a matter of fact, for the specific case of space applications, NAND flash is struggling to keep pace with those advances for multiple reasons. This article provides an overview of these reasons. We discussed the most relevant design aspects to address when dealing with NAND flash memory for the critical space environment. Furthermore, the Sentinel-2 practical example lets the reader experience the advantages of adopting a NAND flash-based SSMM as opposed to a SDRAM-based device. In conclusion, if properly designed, NAND flash currently represents the most suitable candidate as future semiconductor storage technology of upcoming mass memories for space applications.

Description: We, the authors of [1], are appreciative of the positive review of our book given in [2]. Two points were raised in [2] that we found very interesting and important enough to be useful for the audience, so we wanted to further discuss and amplifiy them.

Description: Provides a listing of current committee members and society officers.

Description: Provides a notice of upcoming events of interest to practitioners and researchers.

Description: magazine.periodical.

Description: The book combines contemporary techniques to move from basic image processing techniques of image cues to incorporate context and channels of multimedia content for ambient intelligence. Numerous examples are provided to guide and structure future CV systems based on information fusion of image and nonimaging techniques for a wide domain of users.

Description: Provides a listing of current staff, committee members and society officers.

Description: A novel approach to automatic detection of moving vehicles from a single satellite WorldView-2 imagery is presented. The technique is based on the time gap between three sensor band groups: panchromatic, multispectral, and four new additional multispectral bands. The entire process is automatic and includes movement estimation followed by moving object spectral restoration and construction of spatially built object profiles to estimate the movement direction and velocity. The approach neither relies on external information like road data or site models, nor is limited to vehicle type. The performance of the new approach is demonstrated via detection of several vehicle types on WorldView-2 satellite imagery of the San Francisco area.

Description: Previous works have shown that the combination of vegetation indices with land surface temperature (LST) improves the analysis of vegetation changes. Here, global MODIS-Terra monthly data from 2000 to 2011 were downloaded and organized into LST, NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index) time series. These time series were then corrected from cloud and atmospheric residual contamination through the IDR (iterative Interpolation for Data Reconstruction) method. Then, statistics were retrieved from both corrected time series, and the YLCD (Yearly Land Cover Dynamics) approach has been applied to data sources (NDVI-LST and EVI-LST) to analyze changes in the vegetation. Finally, trends were retrieved and their statistical significance was assessed through the Mann-Kendall statistical framework. Global statistics show that both data sets lead to similar trends, as is the case for the spatial distribution of observed trends. These trends confirm previous results as well as prediction of climate warming consequences, such as a marked increase in boreal temperatures.

Description: Information on the changing land surface is required at high spatial resolutions as many processes cannot be resolved using coarse resolution data. Deriving such information over large areas for Landsat data, however, still faces numerous challenges. Image compositing offers great potential to circumvent such shortcomings. We here present a compositing algorithm that facilitates creating cloud free, seasonally and radiometrically consistent datasets from the Landsat archive. A parametric weighting scheme allows for flexibly utilizing different pixel characteristics for optimized compositing. We describe in detail the development of three parameter decision functions: acquisition year, day of year and distance to clouds. Our test site covers 42 Landsat footprints in Eastern Europe and we produced three annual composites. We evaluated seasonal and annual consistency and compared our composites to BRDF normalized MODIS reflectance products. Finally, we also evaluated how well the composites work for land cover mapping. Results prove that our algorithm allows for creating seasonally consistent large area composites. Radiometric correspondence to MODIS was high (up to ${rm R}^{2}〉0.8$ ), but varied with land cover configuration and selected image acquisition dates. Land cover mapping yielded promising results (overall accuracy 72%). Class delineations were regionally consistent with minimal effort for training data. Class specific accuracies increased considerably ( $sim$ 10%) when spectral metrics were incorporated. Our study highlights the value of compositing in general and for Landsat data in particular, allowing for regional to global LULCC mapping at high spatial resolutions.

Description: Many tropical countries suffer from persistent cloud cover inhibiting spatially consistent reporting of deforestation and forest degradation for REDD+. Data gaps remain even when compositing Landsat-like optical satellite imagery over one or two years. Instead, medium resolution SAR is capable of providing reliable deforestation information but shows limited capacity to identify forest degradation.

Description: The floodplain around Tonle Sap, Cambodia is strongly influenced by seasonal variations in water level. In the wet season, lacustrine landforms and vegetated areas are partly inundated due to increases in the water level. Conversely, they are gradually emerged when the flooding recedes during the dry season. Because floods in Tonle Sap are an annual event, a land cover variation model that takes into account water level is necessary to predict areal changes in each land cover class at the floodplain. To establish this model, we used the Phased Array L-band Synthetic Aperture Radar (PALSAR) backscattering coefficients, normalized difference vegetation index (NDVI) values, and tasseled cap (TC) transformations of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data from 2007 to 2010 to estimate the areal variation of six land cover classes during the annual flood pulse. The radar backscattering coefficients correlated well with NDVI values during the dry season, but the relationship vanished during the wet season. According to our model, a backscattering coefficient change from -8.4 dB to -20.6 dB for lowland shrubs in the flood developing stage corresponded to an areal percentage change of un-flooded lowland shrubs from 16.3% to 0.5% of the total study area. Once the water level increased to the peak of flooding, 46.2% of the lowland shrub area was immersed. Our model also predicted that approximately 41.8% of the total study area was replaced with a water surface at the peak of flooding. When we compared the two results obtained using our model at 6 m above mean sea level (amsl) and using a digital terrain model (DTM) and the land use map, we observed a large difference between the two models in the areal percentage of the corresponding land cover of un-flooded lowland shrubs (-10.3%). Our land cover variation model can be used to predict areal changes in land cover classes during flood development and recession stages, and can also provide ins- ght into flood dynamics, thereby enabling flood management in this region.

Description: With the development of quantitative ocean color remote sensing, estimation of chlorophyll-a concentration in the coastal waters has aroused increasing attention from researchers. Currently, researches are confronted with difficulty in improving the accuracy of chlorophyll-a concentration estimation for turbid waters. Atmospheric correction, chlorophyll-a concentration modeling, and scale effect have already been identified as three critical factors affecting coastal water remote sensing. The in-depth exploration of them will accelerate the research progress of ocean color remote sensing. The ultimate objective of atmospheric correction and scale effect correction is to accurately estimate active constituents of turbid coastal waters in an optical way. Accordingly, the chlorophyll-a concentration modeling is a basic problem to be resolved, while atmospheric correction is the essential one. The scale effect problem arises during the modeling procedure where unrealistic homogeneous assumption is taken to measure chlorophyll-a concentration from the realistic non-homogeneous pixel. In the coastal remote sensing field, these three problems have become the most important topics in the current researches, and they will remain be the hot topics in the future.

Description: Traditional automatic fuzzy clustering methods can obtain the optimal number of clusters by maximizing or minimizing one single-objective function using validity indexes. However, the effectiveness of these methods depends on the selection of the validity indexes, and one single-objective function may not provide satisfactory results because of the complexity of remote sensing images. For instance, the same land types may have different spectral curves, and different land types can have similar curves. To avoid this problem, this paper proposes a novel automatic fuzzy clustering method based on adaptive multi-objective differential evolution (AFCMDE) for remote sensing imagery. In AFCMDE, the automatic clustering problem is transformed into a multi-objective problem using two objective functions: $J_{m}$ and the Xie-Beni index. AFCMDE is designed as a two-layer system comprising an optimization layer and a classification layer. In the optimization layer, AFCMDE searches for a feasible number of clusters by minimizing the $J_{m}$ value and the Xie-Beni index. Based on the obtained number of clusters, AFCMDE utilizes non-dominated and crowd-distance sorting to obtain the optimal clustering centers and output the clustering results. In addition, a self-adaptive strategy without user-defined parameters is also used to improve the differential evolution. Experimental results using three different types of remote sensing image show that the AFCMDE algorithm consistently outperforms the other traditional clustering algorithms and the previous single-objective automatic fuzzy clustering algorithms.

Description: The objective of this research is to investigate the potential of nighttime light images, acquired with Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS), in evaluating global armed conflicts. To achieve this purpose, we assessed the relationship between armed conflicts and the satellite-observed nighttime light variation over 159 countries through annual composites of the nighttime light images. Firstly, a light ratio index was developed to reduce the data inconsistency of annual nighttime light images during 1992–2010. Then 12 countries were selected as examples for a primary investigation, and we found the outbreak of a war can reduce the light and the ceasefire can increase the light from the remote sensing images, which indicates armed conflict events always have significant impact on the nighttime light. Based on this assertion, a nighttime light variation index (NLVI) was developed to quantify the variation of the time series nighttime light. Then using conditional probability analysis, the probability of a country suffering from armed conflicts increases with increase of NLVI. Particularly, when the NLVI value is in a very high level as defined, 80% of the countries have experienced armed conflicts. Furthermore, using correlation analysis, the number of global armed conflicts is highly correlated with the global NLVI in temporal dimension, with a correlation coefficient larger than 0.77. In summary, the potential of nighttime light images in armed conflict evaluation is extended from a regional scale to a global scale by this study.

Description: A rigorous epipolar geometry modeling for synthetic aperture radar (SAR) is developed from a concise imaging model proposed in the paper. The imaging model and epipolar model not only geometrically unify the SAR imaging and the optical camera imaging, but also motivate a 3D target reconstruction which is theoretically validated to be consistent with the radargrammetry and experimentally demonstrated to be accurate.

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Description: The analysis presented in this article shows that the Great Seal bug has a well-coupled eigenmode around 1800 MHz, which is consistent with some of the references. This was probably one of the harmonics of the illumination source, as a high transmit power at the lower frequency would have been easier to generate. In addition, the interaction between acoustically introduced vibrations of the diaphragm would generate an AM signal that could be detected remotely.

Description: In this work, we have developed a multiple UCAVs cooperative air combat simulation platform, which is based on PSO, ACO, and game theory. The Matlab program is used as the developing tool. In this platform, the practitioners can investigate the inherent mechanism by applying game theory to solve the mission decision-making problem of multiple UCAVs in attacking multiple objects. This simulation platform is friendly, easy to use, and easy to modify

Description: The evaluations and results provided in the show that a proper selection of De Bruijn binary sequences as spreading codes in multiuser communications allows to obtain improved performance, with respect to more traditional solutions, like those based on the use of classical Gold codes, m sequences, and OVSF codes. Promising results have been obtained also in DS sequence SS radar simulations, where the number of available sequences may be important to cope with specific scenarios, such as anticollision vehicular radar applications. The behavior exhibited by De Bruijn sequences motivates the efforts aimed at providing an efficient generation algorithm to obtain the complete set of codes for even greater values of n and performing an exhaustive evaluation of their properties. This activity is currently ongoing.

Description: This article presented extended simulations in order to characterize the interference between multiple airborne and terrestrial mobile terminals. The interference analysis is performed considering three cellular networks: (i) GSM-1800 MHz, (ii) UMTS-2100 MHz, and (iii) UMTS-1800 MHz. In the radio interference study, the impact of the onboard base station emissions on the terrestrial mobile terminals was examined and evaluated. The compound interference (in decibels) with the terrestrial terminal was found to follow the Gaussian distribution. The results showed that there is visibility of the onboard networks in terms of noise floor increase, especially for GSM-1800 MHz networks, and the output of the base station should be adjusted around 0 dBm, if we want to avoid using an extra RF shielding in the aircraft cabin. The results for UMTS-2100 or UMTS-1800 networks showed that there is no need for extra attenuation unless the aircraft base station transmits more than 10 dBm. Finally, a general statistical analysis of SIR on a ground mobile station, taking into account the airborne and terrestrial shadowing parameters, was also presented.

Description: Having studied this new book by Clive Alabaster once at Cranfield University but now at White Horse Radar, entitled Pulse Doppler Radar, I can say that I have learned about pulse Doppler radar. Truly I have become a member of the guild of pulse Doppler radar engineers that really know how this radar sees with its electromagnetic eyes.

Description: Recounts the career and contributions of Professor Irving Stoy Reed, November 12, 1923-September 11, 2012.

Description: Ocean color continuity calls for consistent observations from multiple sensors in order to establish a seamless data record to address earth science questions. Currently, both Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on the Terra and Aqua satellites are being operated well beyond their designed five-year mission life, and they have shown signs of sensor degradation. It is thus urgent to evaluate whether the most recently launched Visible Infrared Imager Radiometer Suite (VIIRS) instrument (2011 to present) can provide consistent observations should MODIS instruments stop functioning. In this study, the consistency between MODIS/Aqua and VIIRS measurements over the Tampa Bay estuary $(sim!!1000 hbox{km}^{2})$ is assessed for remote sensing reflectance (Rrs, $hbox{sr}^{ - 1}$ ), chlorophyll-a concentrations (Chla, $ hbox{mg}cdothbox{m}^{ - 3}$ ), and absorption coefficient of colored dissolved organic matter $(a_{rm g}(443), hbox{m}^{ - 1})$ . While Rrs was derived as a standard National Aeronautics and Space Administration product from the SeaDAS software package (reprocessing version R2013.0), Chla and $a_{rm g}(443)$ were estimated using the recently developed regional algorithms for Tampa Bay. Time-series analysis and statistics both showed that the two sensors provided consistent measurements for most products evaluated, with unbiased mean percentage differences of $〈$ 25% and mean annual biases within $-$ 9% (except for one of the eight cases) for large dynamic ranges i- Chla (1.0–20 $hbox{mg}cdot hbox{m}^{ - 3}$ ) and $a_{rm g}(443)$ (0.1–1.5 $hbox{m}^{ - 1}$ ) in all four bay segments. These estimates are comparable or better than those derived from satellite—in situ comparisons, suggesting that VIIRS will provide observations consistent with MODIS, ensuring ocean color continuity and seamless data records for Tampa Bay. Such observations are crucial in establishing a long-term satellite-based water quality decision matrix for Tampa Bay.

Description: In this letter, a synthetic aperture radar (SAR) image registration method based on edge-point features is presented to improve precision, robustness, and applicability of SAR image registration. First, an affine transformation model is decomposed into six parameters with explicit geometric meanings. Next, based on the strength and direction features of the edge point, a matching similarity criterion and a joint similarity metric, i.e., the square summation joint feature, are constructed. Then, the parameters of the transformation model between SAR images are solved with a modified genetic algorithm that is able to get a global optimal solution of the metric. Finally, the performance of the proposed method is validated with two SAR image registration experiments.

Description: In this letter, a novel maximum a posteriori (MAP) filter for synthetic aperture radar (SAR) images is developed. We characterize the return signal of SAR using the Cauchy-Rayleigh mixture model, which is an approximation to the heavy-tailed Rayleigh distribution. The parameters of the Cauchy–Rayleigh mixture model are estimated from the noisy observation by using the expectation–maximization algorithm. Finally, we compare the proposed filter with several classical spatial filtering techniques by applying them on simulated data and various real SAR images. Experimental results show that the Cauchy–Rayleigh-mixture-based MAP filter performs better for speckle removal than the other methods, including Lee, Kuan, and $Gamma$ -MAP.

Description: Land surfaces are commonly obstructed by haze in remote sensing images, which reduces the available land cover information. Haze detection is therefore important for locating, avoiding, or restoring hazy regions. In this letter, a principal component (PC)-based haze masking (PCHM) method is developed for the masking of haze in visible remote sensing images covering land surfaces at middle latitudes. Owing to the evidence of haze in the second PC, the PCHM method results in accurate haze masks. The complete procedure comprises two steps: haze construction and spatial optimization. The validity of the PCHM method is demonstrated through its application to several hazy visible images clipped from Landsat Enhanced Thematic Mapper Plus scenes. The quantitative assessments verify the superiority of the proposed method over the haze optimized transformation method for the production of binary haze masks. In addition, the resulting haze masks are compared with a MODIS cloud product, which further proves the necessity and validity of the proposed method.

Description: Spectral unmixing amounts to automatically finding the signatures of pure spectral components (called endmembers in the hyperspectral imaging literature) and their associated abundance fractions in each pixel of the hyperspectral image. Many algorithms have been proposed to automatically find spectral endmembers in hyperspectral data sets. Perhaps one of the most popular ones is the pixel purity index (PPI), which is available in the ENVI software from Exelis Visual Information Solutions. This algorithm identifies the endmembers as the pixels with maxima projection values after projections onto a large randomly generated set of random vectors (called skewers). Although the algorithm has been widely used in the spectral unmixing community, it is highly time consuming as its precision asymptotically increases. Due to its high computational complexity, the PPI algorithm has been recently implemented in several high-performance computing architectures, including commodity clusters, heterogeneous and distributed systems, field programmable gate arrays, and graphics processing units (GPUs). In this letter, we present an improved GPU implementation of the PPI algorithm, which provides real-time performance for the first time in the literature.

Description: This letter is dedicated to a generic approach for the automated detection and classification of man-made objects in urban corridors from point clouds acquired by vehicle-borne mobile laser scanning (MLS). The approach is designed based on a priori knowledge in urban areas: 1) man-made objects feature geometric regularity such as vertical planar structures (e.g., building facades), whereas vegetation reveals huge diversity in shape and point distribution and 2) different types of urban man-made objects can be characterized by the point extension and the height above the ground level. Therefore, MLS-based point clouds are first divided into three layers with respect to the vertical height. In each layer, seed points of man-made objects are indicated by a line filter in the footprints of off-ground objects, which is generated by binarizing the spatial accumulation map of the point clouds. These seed points are further classified by examining in which layers the seed points of objects are found. Finally, points belonging to respective objects can be retrieved based on the classified seed points. The experiments show that various man-made objects on both sides of the street can be well detected, with a detection rate of up to 83%. For the classification of detected urban objects, overall accuracy of 92.37% can be achieved.

Description: In this letter, an innovative technique for change detection in urban areas using very high resolution synthetic aperture radar multichannel stacks is proposed. Instead of using the amplitude image, as in classical change detection approaches, the proposed technique uses the full complex image in a Markovian framework. The complex data are modeled using Markov random field hyperparameters, which are particular local parameters that take into account the spatial correlation between pixels. Starting from two data sets, the pre- and the postevent ones, the proposed algorithm, first, estimates the two hyperparameter maps and, then, compares the similarity between them. If a change occurs between the pre- and the postevent acquisitions, the statistical distribution of the hyperparameter maps will change. The maximum distance between the two obtained statistical distributions provides an index of changes. This sort of spatial correlation maps is computed using statistical estimation techniques, while the similarity comparison is computed using the two-step Kolmogorov–Smirnov statistic test. The algorithm is validated on simulated data and tested on real COSMO-SkyMed data acquired on the area of Naples, showing interesting and promising results.

Description: Circular or directional data are used in disciplines such as meteorology, geomatics, biology, and geology. The analysis of angular data requires special methods that are available in some statistical packages. However, these tools analyze only the angular values and do not include the vector modules, assuming unit vectors in all cases. In this letter, an open-source graphic and statistical package, i.e., VecStatGraphs2D, is described. It works in the R environment and provides statistics and graphics for modules (linear) and azimuths (circular), as well as graphics for the joint analysis of modules and azimuths. QuikSCAT satellite wind data are used to demonstrate some features of the package. QuikSCAT data are non-unit-length vectors, where both azimuth and magnitude (speed) are derived from $u$ and $v$ vector components (vector projections over the $x$ - and $y$ -axes). The example is used to show the seasonal change of winds in the Intertropical Convergence Zone, a key area in the ocean bird migration from the North to South Atlantic oceans.

Description: The sparsity of targets intrinsically paves a new way to apply compressive sensing (CS) to inverse SAR (ISAR) imaging. However, in the CS-based ISAR imaging system, the ISAR image is considered as a vector composed of random and independent scattering points, and the dependence between pixels is ignored, which always results in the degradation of the shape and geometry of targets, especially when the number of CS measurements and the signal-to-noise ratio are small. In this letter, a novel ISAR imaging framework is proposed via a combination of local sparsity constraint and nonlocal total variation (NLTV). The sparsity is a form prior that the number of strong scattering points is smaller than that of pixels in the image plane. It plays the role of classification of the strong scattering point from the clutter background. NLTV aims to suppress the noise and to remove some false strong scattering centers or clutter and simultaneously preserves the shape and geometry of target regions. Experiments on real data confirm the proposed method's validity.

Description: This letter considers the problem of building layout determination using through-wall-radar imaging technology, which employs multiple transmit–receive channels to implement multiview synthetic aperture imaging. In each view, the phase errors of multichannel data introduced by the unknown walls deteriorate significantly the performance of the coherently data-combined algorithm by Le Herein, we first obtain multiple single-channel building layout images for all independent channels of each view and propose a novel noncoherent fusion method named multiply–subtract–add to combine them into a single-view layout image. Then, we present an $M$ – $N$ – $K$ detector plus median filtering to fuse multiple single-view layout images and reduce the existing cavities and burrs of wall images. The experimental results reveal that the presented noncoherent image fusion method gives the single-view layout images with higher signal-to-clutter-and-noise ratio than the conventional coherent algorithm based on data combination and a near-tidy panorama layout image is generated almost without the cavities and burrs.

Description: While an increasing number of satellite images are collected over a regular period in order to provide regular spatiotemporal information on land-use and land-cover changes, there are very few compression schemes in remotely sensed imagery that use historical data as a reference. Just as individual images can be compressed for separate transmission by taking into account their inherent spatial and spectral redundancies, the temporal redundancy between images of the same scene can also be exploited for sequential transmission. In this letter, we propose a nonlinear elastic method based on the general relationship to predict adaptively the current image from a previous reference image without any loss of information. The main feature of the developed method is to find the best prediction for each pixel brightness value individually using its own conditional probabilities to the previous image, instead of applying a single linear or nonlinear model. A codebook is generated to record the nonlinear point-to-point relationship. This temporal lossless compression is incorporated with spatial- and spectral-domain predictions, and the performances are compared with those of the JPEG2000 standard. The experimental results show an improved performance by more than 5%.

Description: In this letter, a new method for Kennaugh matrix decomposition is proposed, and a new norm for the Kennaugh matrix is defined. The Kennaugh matrix is decomposed into two parts: The first is a coherent target matrix, and the second is a residual matrix with minimum norm. The properties of the extracted coherent target are discussed, and an application of the extracted coherent target is implemented. In this application, an incoherent image is converted into a coherent image. Single-look sphere–diplane–helix decomposition is then performed. An experiment on Airborne SAR polarimetric data over San Francisco has been carried out, thus demonstrating the effectiveness of the application.

Description: This letter describes a method to extract the unknown parameters of the formation using triaxial induction data based on the whole-space model. Two single-parameter coupled functions are constructed to determine the horizontal conductivity and the relative dip. After that, the vertical conductivity is solved analytically using another single-parameter function. The method is extremely fast and can be implemented for real-time processing and interpretation to allow decision making on the well site. The results can be further refined via postprocessing while the relative dip is required to be constant within an interval.

Description: A reduced-rank framework with set-membership filtering (SMF) techniques is presented for adaptive beamforming problems encountered in radar systems. We develop and analyze stochastic gradient (SG) and recursive least squares (RLS)-type adaptive algorithms, which achieve an enhanced convergence and tracking performance with low computational cost, as compared with existing techniques. Simulations show that the proposed algorithms have a superior performance to prior methods, while the complexity is lower.

Description: The problem of state space estimation of linear systems in an unknown non-Gaussian noise field is considered. A finite Gaussian mixture model (GMM) is used to model the non-Gaussian measurement noise with unknown statistics. A variational Bayesian expectation maximization (VBEM) algorithm is proposed to estimate the system states as well as the unknown parameters. In the variational Bayesian expectation (VBE) step, approximate inference is established to estimate the system state. The Gaussian mixture parameters are then updated in the variational Bayesian maximization (VBM) step. We also derive the true marginal posteriors to verify the performance of the proposed VBEM method. Computer simulations show that the proposed method has an improved estimation performance compared with other conventional approaches.

Description: A variable-structure multiple-model (VSMM) approach, named equivalent-model augmentation (EqMA), is proposed. Here the model set is augmented by a variable model intended to best match the unknown true mode. To fully utilize the information provided by model sequences (model histories), this variable model depends on the true mode at the previous time. Thus different previous models correspond to different augmenting models. To make the estimation process computationally feasible, the unknown variable model at the previous time is approximated by an equivalent model (EqM) which provides the closest estimation results in the sense of minimum Kullback-Leibler (KL) divergence. EqM also contains the online information provided by the measurements. Performance of the proposed EqMA approach is evaluated via two scenarios of maneuvering target tracking. Simulation results demonstrate the effectiveness of EqMA compared with the interacting multiple-model (IMM) algorithm and the expected-mode augmentation (EMA) algorithm.

Description: A path-planning algorithm to guide unmanned aerial vehicles (UAVs) for tracking multiple ground targets based on the theory of partially observable Markov decision processes (POMDPs) is presented. A variety of features of interest are shown to be easy to incorporate into the framework by plugging in the appropriate models, which demonstrates the power and flexibility of the POMDP framework. Specifically, it is shown how to incorporate the following features by appropriately formulating the POMDP action space, transition law, and objective function: 1) control UAVs with both forward acceleration and bank angle subject to constraints; 2) account for the effect of wind disturbance on UAVs; 3) avoid collisions between UAVs and obstacles and among UAVs; 4) track targets while evading threats; 5) track evasive targets; and 6) mitigate track swaps.

Description: A new approach for multitarget detection and tracking with Doppler ambiguity is presented. Ambiguous Doppler measurements, in addition to the position measurements, are directly used in data association and tracking. Based on the unscented Kalman filter (UKF), the multiple hypothesis tracking (MHT) algorithm and the probabilistic data association (PDA) algorithm, three different methods for solving the ambiguity, independent of the choice of particular pulse repetition frequency (PRF) values, in the tracking level are proposed. First, the UKF is modified to handle explicitly the ambiguous Doppler measurement. It is shown that the modified UKF can achieve better tracking performance than the standard UKF. On the other hand, the MHT and PDA algorithms, both of which are usually used to solve the measurement-to-track association problem, are modified here to handle the Doppler ambiguity problem. Simulations are performed to demonstrate the effectiveness of the new algorithms.

Description: The performance of postdetection integration (PDI) techniques for the detection of Global Navigation Satellite Systems (GNSS) signals in the presence of uncertainties in frequency offsets, noise variance, and unknown data-bits is studied. It is shown that the conventional PDI techniques are generally not robust to uncertainty in the data-bits and/or the noise variance. Two new modified PDI techniques are proposed, and they are shown to be robust to these uncertainties. The receiver operating characteristics (ROC) and sample complexity performance of the PDI techniques in the presence of model uncertainties are analytically derived. It is shown that the proposed methods significantly outperform existing methods, and hence they could become increasingly important as the GNSS receivers attempt to push the envelope on the minimum signal-to-noise ratio (SNR) for reliable detection.

Description: Use of radio waves is a very common choice for proximity fuzes. The stand-off distance for fuzes varies between a lower and an upper limit depending on the electromagnetic reflectivity of terrain (target). If the reflectivity of the terrain is comparably higher, the return power at the receiver reaches to the threshold level more quickly and the stand-off distance increases. The work presented here introduces an approach to estimate the height of airborne ballistic platforms more precisely compared with the conventional fuzes regardless of the reflection properties of the terrain. Independent measurements of the rate of change of voltage from the receiver and the rate of change of height (from Doppler shift) allow the height to be estimated when the terrain reflectivity is unknown.

Description: The cardinality balanced multitarget multi-Bernoulli (CBMeMBer) filter is a recursive, multitarget tracking mechanism based on the random finite set (RFS) theory using the finite set statistics (FISST) framework. It provides an estimate of the number of targets in a given scenario space, along with the most likely locations of those targets. It also provides this estimate without the expensive operation of multidimensional assignment between measurements and target estimates. Unlike other RFS methods, the CBMeMBer filter outputs an estimate of the actual multitarget probability density function. Current implementations include a nonlinear sequential Monte Carlo (SMC) approximation, as well as an analytical Gaussian mixture (GM) solution. A new MeMBer recursion for tracking multiple targets traveling under multiple motion models is introduced. The multiple model CBMeMBer (MM-CBMeMBer) filter presented here uses jump Markov models (JMM) to extend the standard CBMeMBer recursion to allow for multiple target motion models. This extension is implemented using both the SMC- and GM-based CBMeMBer approximations. The recursive prediction and update equations are presented for both implementations. Each multiple model implementation is validated against its respective standard CBMeMBer implementation, as well as against each other. This validation is done using a simulated scenario containing multiple manoeuvering targets. A variety of metrics, including estimate accuracy, model detection capability, and algorithm computational efficiency are used for performance evaluation. The new method is shown to improve results in several metrics with only a minor increase in computational complexity.

Description: Amplitude and phase errors can cause mismatches between the hypothesized signal vector and the steering vector of an antenna array. A generalized likelihood ratio test (GLRT) for signal detection with multiple coherent processing intervals (CPIs) in unknown interference is derived. The detection performance of the resulting adaptive noncoherent integration (NCI) algorithm is analyzed along with the effect of steering vector mismatch on detection performance. Detection performance and the effect of steering vector mismatch are illustrated with results for both a nonfluctuating signal model and a CPI-to-CPI fluctuating signal model. It is shown that a signal-to-interference-plus-noise ratio (SINR) loss factor inherent to the algorithm in effect limits the performance gain due to NCI of mismatched signals.

Description: A novel estimation method for the 6 degree-of-freedom (6-DOF) motion of a target moving with slowly varying velocity is proposed, which is based on the monocular image sequences from a moving camera. The homography matrix for moving targets is derived. It is shown that the dynamic model of the target in 6-DOF motion can be described as a linear time-varying system. Consequently, the Kalman filter can be directly applied. Our estimation method does not need a priori information of target depth or size. The observability condition of our estimator is much milder than that of the previously known bearings-only tracking methods.

Description: The detection, localization, and tracking performance of multiple input-multiple output (MIMO) radars with widely separated antennas is investigated and compared with that of multistatic radar systems. A multiple-hypothesis (MH)-based algorithm is proposed for multitarget localization for the case where extended targets with multiple spatial reflections become unobservable in certain transmitter-receiver pairs. A particle filter (PF)-based algorithm is then proposed to handle dynamic multitarget tracking. Finally, simulation results are provided to demonstrate the relative capability of MIMO radars in localizing and tracking extended targets under various signal-to-noise ratio (SNR) conditions compared with multistatic radars.

Description: The problem of estimating the state of a nonlinear stochastic plant is considered. Unlike classical approaches such as the extended Kalman filter, which are based on the linearization of the plant and the measurement model, we concentrate on the nonlinear filter equations such as the Zakai equation. The numerical approximation of the conditional probability density function (pdf) using ordinary grids suffers from the "curse of dimension" and is therefore not applicable in higher dimensions. It is demonstrated that sparse grids are an appropriate tool to represent the pdf and to solve the filtering equations numerically. The basic algorithm is presented. Using some enhancements it is shown that problems in higher dimensions can be solved with an acceptable computational effort. As an example a six-dimensional, highly nonlinear problem, which is solved in real-time using a standard PC, is investigated.

Description: Automatic target recognition (ATR) using high range resolution (HRR) radar signatures is developed using classical Bayesian multiple hypothesis theory. An eigen-template-based matched filtering (ETMF) algorithm is presented where the templates are formed using the dominant range-space eigenvector of detected HRR training profiles and classification is performed using normalized matched filtering (MF). The proposed approach is extended to multi-look and sequential ATR where new observation profiles are recursively combined probabilistically with previous steps to update ATR results, which is useful for simultaneous recognition and tracking of moving targets. An HRR-specific profile normalization scheme is presented to satisfy matched filter requirements. Classification performance of the proposed method has been compared with a linear least-squares method and hidden Markov model (HMM) approach using MSTAR data collection.

Description: Software-based Global Positioning System (GPS) receivers have been recognized as an effective research platform in recent years. The impact of oscillator frequency offset on hardware receiver and software receiver signal processing is contrasted based on a refined signal model and cross correlation function (CCF) analysis. Several online clock error correction algorithms are presented to produce unbiased measurements and clock error estimates with known and unknown front end frequency plans and with and without signal tracking and navigation solutions for single- and dual-frequency receivers on both static and dynamic platforms. The CCF formulation and the clock correction performance are validated using simulated signals and real single- and dual-frequency GPS data. The raw frequency error measurements with 0.02 s time resolution for an oven controlled crystal oscillator (OCXO) using real GPS signal report an Allan deviation (ADEV) of 1.3E-11 and a standard deviation of 1.56E-11.

Description: This paper presents a closed form expression for estimating the signal-to-clutter improvement of a digital signal processor. The expression is unique in that it combines a number of factors (analog-to-digital converter dynamic range, phase noise, integration gain, automatic gain control effects, and clutter filter specifications) that have historically been treated separately. Two standard cases are shown as subsets of the general expression: signal-to-clutter improvement for analog signal processors and moving target indicator-type digital signal processors.

Description: The performance of postdetection integration (PDI) techniques for the detection of Global Navigation Satellite Systems (GNSS) signals in the presence of uncertainties in frequency offsets, noise variance, and unknown data-bits is studied. It is shown that the conventional PDI techniques are generally not robust to uncertainty in the data-bits and/or the noise variance. Two new modified PDI techniques are proposed, and they are shown to be robust to these uncertainties. The receiver operating characteristics (ROC) and sample complexity performance of the PDI techniques in the presence of model uncertainties are analytically derived. It is shown that the proposed methods significantly outperform existing methods, and hence they could become increasingly important as the GNSS receivers attempt to push the envelope on the minimum signal-to-noise ratio (SNR) for reliable detection.

Description: A variable-structure multiple-model (VSMM) approach, named equivalent-model augmentation (EqMA), is proposed. Here the model set is augmented by a variable model intended to best match the unknown true mode. To fully utilize the information provided by model sequences (model histories), this variable model depends on the true mode at the previous time. Thus different previous models correspond to different augmenting models. To make the estimation process computationally feasible, the unknown variable model at the previous time is approximated by an equivalent model (EqM) which provides the closest estimation results in the sense of minimum Kullback-Leibler (KL) divergence. EqM also contains the online information provided by the measurements. Performance of the proposed EqMA approach is evaluated via two scenarios of maneuvering target tracking. Simulation results demonstrate the effectiveness of EqMA compared with the interacting multiple-model (IMM) algorithm and the expected-mode augmentation (EMA) algorithm.

Description: In the work presented here we explore the interest of target localization using multiple-input multiple-output (MIMO) radar systems with widely separated antennas. The time delays between every transmit/receive antenna are an integral part of target localization using MIMO radar systems. We assume that these time delays have already been estimated by a preprocessing algorithm. Accordingly, we propose a target localization algorithm using the least squares estimation which has a closed form without requiring any initial condition. Simulation results evaluate the performance of the proposed method in comparison with the Cramer-Rao lower bound (CRLB) and the best linear unbiased estimator (BLUE) method. The effect of the arrangement and number of transmitters and receivers on the algorithm performance is also investigated.

Description: The work presented here is concerned with performance analysis for data association, in a target tracking environment. Effects of misassociation are considered in a simple (linear) multiscan framework so as to provide closed-form expressions of the probability of correct association. We focus here on the development of explicit approximations of this probability. Via rigorous calculations the effect of dimensioning parameters (number of scans, false measurement positions or densities) is analyzed, for various modelings of the false measurements. Remarkably, it is possible to derive very simple expressions of the probability of correct association which are independent of the scenario kinematic parameters.

Description: Combining line-of-sight (LOS) measurements from passive sensors (e.g., satellite-based IR, ground-based cameras, etc.), assumed to be synchronized, into a single composite Cartesian measurement (full position in 3D) via maximum likelihood (ML) estimation, can circumvent the need for nonlinear filtering??which involves, by necessity, approximations. This ML estimate is shown to be statistically efficient, even for small sample sizes (as few as two LOS measurements), and as such, the covariance matrix obtainable from the Cramer-Rao lower bound (CRLB) provides the correct measurement noise covariance matrix for use in a target tracking filter.

Description: Automatic extraction of building outlines from airborne laser scanning (ALS) point clouds has been an active topic in the field of photogrammetry, remote sensing, and computer vision. In this letter, a marked point process method is implemented to extract building outlines from ALS point clouds. First, the Gibbs energy model of building objects is defined to describe the building points. Second, the defined Gibbs energy model is sampled within the framework of reversible-jump Markov chain Monte Carlo and optimized to find an optimal energy configuration by simulated annealing. Finally, the detected building objects are refined to eliminate false detections, and the outlines of buildings are derived from the detected building objects by morphological operators. The standard data set provided by ISPRS is used to verify the validity of the proposed method. The method extracted building objects from the standard data sets with an average completeness of 87.3% and correctness of 91.57% at the pixel level, and an average completeness of 77.6% (97.3%) and correctness of 98.1% (97.9%) at the object $(hbox{object} > 50 hbox{m}^{2})$ level.

Description: An automated approach to extracting 3-D individual trees in urban areas is developed based on jointly analyzing airborne LiDAR data and imagery. First, the spectral, geometric, and spatial context attributes are defined and integrated at the LiDAR point level. Then, a binary AdaBoost classifier is used to separate points belonging to trees from other urban objects. Once the classification is completed, a spectral clustering method by applying the normalized cuts to a graph structure of point clouds of the vegetation class is performed to segment single trees. The geometric and spectral attributes play an important role in establishing the weight matrix, which measures the similarity between every two graph nodes and determines the cut function. The performance of the approach is validated by real urban data sets, which were acquired over two European cities. The results show that 3-D individual trees can be detected with mean accuracy of up to 0.65 and 0.12 m for tree position and height. Based on the results of this work, geometric and biophysical properties of individual trees can be further retrieved.

Description: Incorporated with digital beam-forming processing, multichannel spaceborne synthetic aperture radar (SAR) systems are able to overcome the minimum antenna area constraint and yield high resolution and wide swath (HRWS) images. This letter mainly investigates the performance of the space–time adaptive processing (STAP) approach applied to HRWS SAR imaging. The analytic expressions for the signal-to-noise ratio (SNR) scaling factor and azimuth ambiguity to signal ratio (AASR) are derived and confirmed by the simulated results. Then, the influence of channel errors on HRWS imaging is analyzed in detail.

Description: Many geometric distortions, such as earth curvature distortion and length distortion, exist in the map projection frame. Therefore, in aerial photogrammetry, if direct georeferencing is performed in the map projection frame, the crucial work becomes compensating for the effect of the various geometric distortions. This letter mainly focuses on length distortion and proposes two new correction approaches: the changing image coordinates method and the changing object coordinates method. The experimental results show that the changing object coordinates method is less influenced by terrain fluctuation, and its correction accuracy is therefore commonly higher than the changing image coordinates method as well as two existing approaches (i.e., the changing flight height method and the changing focal length method).

Description: Under the condition of time variant RFI, the limitation of training sample size causes great performance degradation of the conventional Radio Frequency Interference (RFI) suppression algorithm based on eigen-subspace projection (ESP) method. A novel RFI suppression method using iterative adaptive approach (IAA) and orthogonal subspace projection (OSP) method is proposed for synthetic aperture radar (SAR). Dispensing with parametric search and model order estimation, the proposed method estimates the RFI power spectrum adaptively and iteratively, utilizing few training samples and filtering the RFI based on the OSP method. Both the simulation and experimental results are provided to illustrate the performance of the proposed method.

Description: A new cross-channel interference suppression method based on the fractional Fourier transform (FRFT) is proposed to eliminate the effect due to cross-channel interference when a full-polarimetric radar adopts the opposite-slope linear frequency modulation transmit signals and de-ramping processing in the simultaneous measurement mode. The FRFT with an appropriate order is operated on the signals in the cross-interference interval. Such order is obtained via the calculation of the frequency-modulation slope of transmitted signals. The cross-channel interference is changed into sinc functions that are detected by a designed threshold and filtered out in the FRFT domain to suppress the cross-channel interference. The simulations show that the proposed method can obviously reduce the peak sidelobe levels and the integrated sidelobe levels of targets in range profiles compared with the traditional method, which is benefit to improve the detection and measurement performances of weak polarimetric scattering components. The proposed method is suitable for stable or slow-moving targets.

Description: This work has been conducted in the framework of several projects devoted to assess the performance of the Soil Moisture and Ocean Salinity (SMOS) mission full-pol measurement mode. Since its launch in November 2009, SMOS is producing dual-polarization brightness temperature synthesized images that are yielding a high scientific return. However, these images are affected by a non-negligible spatial amplitude error, the so-called spatial bias (SB), that degrades geophysical parameter retrieval. This effect is particularly detrimental in SMOS polarimetric images where spatial bias is masking the polarimetric physical signature to a large extend. This paper presents a method to mitigate SMOS spatial bias by taking into account the co- and cross-polar antenna patterns in the image reconstruction algorithm through the, so called, full-pol G-matrix (FPG). The method is validated by producing spatial bias maps out of the comparison between SMOS full-pol images and an accurate polarimetric brightness temperature model of the ocean. This model has been provided to SMOS ESLs (Expert Support Laboratories) by LOCEAN (Laboratoire d'Océanographie et du Climat, France) as a test bench to validate and improve SMOS Level 1 (L1) data. Finally, a radiometric performance summary table comparing spatial bias and radiometric sensitivity between this new FPG approach and SMOS current co-polar G-matrix approach (CPG) is provided. This paper presents the best quality SMOS polarimetric images, which may lead a breakthrough in the science returns of the mission.

Description: In this letter, we investigate the subject of joint angle and array gain-phase error estimation for bistatic multiple-input multiple-output radar, and propose a joint scheme for angle and array gain-phase error estimation based on trilinear decomposition. The estimations of transmit and receive direction matrices are primarily obtained via trilinear decomposition, after which the optimization problem for estimating array gain-phase errors and angles can be constructed. Array gain-phase error vectors are obtained by Lagrange multipliers, and the angles are estimated according to the estimated gain-phase errors. In contrast to the ESPRIT-like algorithm, the proposed method not only obtains automatically paired estimations of the angles but also has much better performance for angle and array gain-phase error estimation. Simulations verify the effectiveness of our approach.

Description: Certain characteristics of a material such as the surface reflectivities can be determined even without knowledge of its internal properties. In this letter, a direct relationship (Azzam relationship) and an analytical approximation (Azzam–Sohn–Hong (ASH) approximation) between the vertically and horizontally polarized reflectivities of specular surfaces are derived and validated using the refractive indices of water and metal in a variety of spectral regions. For the purpose of practical remote applications, land, sea water, sea ice, and oil surfaces are considered and compared using the Hong and ASH approximations. Consequently, ASH approximation has an advantage in a variety of spectral bands for materials with a small imaginary part of refractive index, while the Hong approximation does well in the microwave spectral region, or when the imaginary part of the reflective index is not neglected. Thus, a combination of the Hong and ASH approximations is suggested to improve upon previous studies that used only the Hong approximation in a variety of applications.

Description: A rice-planted area map is a basic information resource for rice production management. Synthetic aperture radar (SAR) is an appropriate technique for rice mapping and so far is mostly based on extracting time series changes of backscattering $(sigma^{0})$ in a rice-planted area. However, sometimes there is not enough data to extract the $sigma^{0}$ curve for the area. To overcome this problem of a lack of data, we propose a method to detect rice-planted area by using small sets of multi-temporal SAR data. This method also addresses the fluctuation of $sigma^{0}$ values between SAR measurements. We have applied the method using multi-temporal ALOS/PALSAR data acquired over five years during the dry season. The rice-planted area was well detected and the viability of this method was demonstrated.

Description: Airborne synthetic aperture radar (SAR) raw data are affected by motion errors. These are commonly accounted for via standard two-step motion compensation (MOCO) algorithm followed by the Precise Topography- and Aperture-dependent (PTA) procedure proposed some years ago. In this letter, we show how the azimuth-to-frequency mapping used by the PTA approach should be modified to fully account for the presence of uncompensated motion errors.