ALBERT

Beschreibung: Postdisaster search and rescue is an important application of ultrawideband (UWB) radar systems, which mainly detect trapped victims by their respiratory-motion response. The development of a respiratory-motion detection (RMD) algorithm that can eliminate nonstationary clutter and noise is a challenging task for the application. A new algorithm is proposed to deal with the task in this letter. It uses the multichannel singular spectrum analysis (MSSA) technique to reconstruct the respiratory-motion response detected by a UWB radar. During the reconstruction, the periodicity and range interrelation characteristics of the response are exploited to adaptively identify signal subspaces. The performance of the algorithm is verified both by simulated and real data. The results show its improved performance over the reference algorithms, e.g., a singular-value-decomposition-based algorithm. The adaptive-MSSA-based RMD algorithm has great promise not only in practical use but also for future research of UWB-radar-based human being remote sensing.

Beschreibung: Land–sea contamination observed in Soil Moisture and Ocean Salinity (SMOS) brightness temperature images is found to have two main contributions: the floor error inherent of image reconstruction and a multiplicative error either in the antenna temperature or in the visibility samples measured by the correlator. The origin of this last one is traced down to SMOS calibration parameters to yield a simple correction scheme, which is validated against several geophysical scenarios. Autoconsistency rules in interferometric synthesis together with redundant and complementary calibration procedures provide a robust SMOS calibration scheme.

Beschreibung: This letter proposes a novel algorithm, which is based on the generalized method of moments (GMM), for the estimation and correction of phase errors induced in synthetic aperture radar (SAR) imagery. The GMM algorithm is used to replace the original phase-estimation kernel in the basic structure of the phase-gradient-autofocus algorithm. Since this novel algorithm does not require the observed signal to be a certain distribution model, it is able to estimate arbitrary phase errors. The GMM algorithm has the ability of estimating range-dependent phase errors, which makes it an efficient estimator. As a result, higher accuracy of the estimated phase errors and a better focused image can be achieved. Excellent results have been obtained in autofocusing and imaging experiments on real SAR data.

Beschreibung: Strong clutter reflections of terrain and marine surfaces obscure the contrast between the target-of-interest and clutter (terrain and marine surface reflections) in synthetic aperture radar (SAR) images and consequently hinder the efficiency of image interpretation and analysis. To overcome this problem, this letter proposes an efficient clutter suppression method in SAR images, which is named shedding irrelevant patterns (SIP). The essence is to construct a regression function that can suppress clutter and preserve the target patterns concurrently. We assume that the clutter is irrelevant to the target-of-interest and distinguishable in patterns in terms of image-pixel distribution and intensity (spatial information). Experimental results show the efficiency of the proposed method in both clutter suppression and target pattern preservation.

Beschreibung: A concern in hyperspectral image classification is the high number of required training samples. When traditional classifiers are applied, feature reduction (FR) techniques are the most common approaches to deal with this problem. Subspace-based classifiers, which are developed based on high-dimensional space characteristics, are another way to handle the high dimension of hyperspectral images. In this letter, a novel subspace-based classification approach is proposed and compared with basic and improved subspace-based classifiers. The proposed classifier is also compared with traditional classifiers that are accompanied by an FR technique and the well-known support vector machine classifier. Experimental results prove the efficiency of the proposed method, especially when a limited number of training samples are available. Furthermore, the proposed method has a very high level of automation and simplicity, as it has no parameters to be set.

Beschreibung: To improve the spatial density of measurement points of persistent-scatterer interferometry, distributed scatterer (DS) should be considered and processed. An important procedure in DS interferometry is the phase triangulation (PT). This letter introduces two modified PT algorithms (i.e., equal-weighted PT and coherence-weighted PT) and analyzes the mathematical relations between different published PT methods (i.e., the maximum-likelihood phase estimator, least squares estimator, and eigendecomposition-based phase estimators). The analysis shows that the above five PT methods share very similar mathematical forms with different weight values in the estimation procedure.

Beschreibung: Sparse representation-based classifier and its variants have been widely adopted for hyperspectral image (HSI) classification recently. However, sparse representation is unstable so that similar features might obtain significantly different sparse codes. Despite the instability, we find that the sparse codes follow a class-dependent distribution under the structured dictionary consisting of training samples from all classes. Based on this observation, a novel discriminative feature, sparse code histogram (SCH), is developed for HSI classification. By counting the SCH of each sample from the sparse codes of its spatial neighbors, we can statistically obtain the distribution pattern of sparse codes of the class to which the sample belongs, and then treat the SCH as a new feature for classification. To reduce the possible outliers among the neighbors, a shape-adaptive neighborhood extractor is also employed to enhance the stability of the histogram feature. Experimental results demonstrate that SCH enjoys a strong discriminative power, which can achieve notably better performance than several state-of-the-art methods for HSI classification with limited training samples.

Beschreibung: Several detection statistics have been proposed for detecting fine ground disturbances between two synthetic aperture radar (SAR) images, such as vehicle tracks. The standard method involves estimating a local correlation coefficient between images. Other methods have been proposed using various statistical hypothesis tests. One of these alternative methods is a generalized likelihood ratio test (GLRT), which compares a full-correlation image model to a no-correlation image model. In this letter, we expand the GLRT to polarimetric SAR data and derive the appropriate GLRT detection statistics. Additionally, we explore relaxing the equal variance/equal polarimetric covariance assumptions used in previous results and find improved performance on macroscopic scene changes.

Beschreibung: For downward-looking linear array 3-D synthetic aperture radar, the resolution in cross-track direction is much lower than the ones in range and azimuth. Hence, superresolution reconstruction algorithms are desired. Since the cross-track signal to be reconstructed is sparse in the object domain, compressive sensing algorithm has been used. However, the imaging processing on the 3-D scene brings large computational loads, which renders challenges in both data acquisition and processing. To cover this shortage, truncated singular value decomposition is utilized to reconstruct a reduced-redundancy spatial measurement matrix. The proposed algorithm provides advantages in terms of computational time while maintaining the quality of the scene reconstructions. Moreover, our results on uniform linear array are generally applicable to sparse nonuniform linear array. Superresolution properties and reconstruction accuracies are demonstrated using simulations under the noise and clutter scenarios.

Beschreibung: This letter proposes a signal processing method of passive bistatic radar (PBR) exploiting an uncooperative radar as an illuminator. Compared with other opportunity illuminators, the transmitting signal of a radar usually has a better ambiguity function, which leads to a higher range resolution. Two channels are needed in PBR system. The reference channel is used to estimate radar signal parameters and reconstruct directly propagated signal. The surveillance channel is used to receive scattered wave. An array antenna and a simultaneous multibeam algorithm are necessary in the surveillance channel due to the flexible beam scanning of the uncooperative radar. The procedure of the proposed method is explained in detail, which is then followed by a field experiment. Preliminary results from the field experiment show that the proposed method can be applied to target angle and bistatic range measurement successfully.

Beschreibung: In order to achieve 3-D imaging with an airborne down-looking linear-array synthetic aperture radar (LASAR), a uniform virtual antenna array may be obtained by aperture synthesis of the cross-track sparse multiple-input–multiple-output array. However, the actual 3-D imaging quality is unavoidably degraded by errors in the virtual element position. In this letter, we investigate the effects of these errors on the forms and the degrees of image quality degradation by decomposing the error-related stochastic processes via an orthogonal transform based on discrete Legendre polynomials. It should be noted that these analyses are helpful for designing a LASAR system and providing a reference for specifying the requisite precision of measurement devices and calibration methods. Finally, we briefly consider the use of calibration methods to eliminate the effects of errors.

Beschreibung: In problems where labeled data are scarce, semisupervised learning (SSL) techniques are an attractive framework that can exploit both labeled and unlabeled data. These approaches typically rely on a smoothness assumption such that examples that are similar in input space should also be similar in label space. In many domains, such as remotely sensed hyperspectral image (HSI) classification, the data violate this assumption. In response, we propose a general method by which a neighborhood graph used in SSL is learned using binary classifiers that are trained to predict whether a pair of pixels shares the same label. Working within the framework of semisupervised neural networks (SSNNs), we show that our approach improves on the performance of the SSNN on two HSI data sets.

Beschreibung: In this letter, new models for the spatial correlation of sea clutter texture and intensity are proposed as improved versions of current power law models or exponential decay model. The models for texture have three unknown parameters, and thus can be called triparametric models. The structure of the models is a weighted sum of two components, which can describe the decaying process of the correlation coefficient with spatial lags, as well as the periodic behavior due to the existence of transient coherent structures in sea clutter. Unknown parameters are optimized by the nonlinear least square fit method. Models for sea clutter intensity can be obtained through a linear transform for uncorrelated speckle based on the compound-Gaussian representation of sea clutter. The proposed models are validated and compared with current models using S- and C-band measured sea clutter data. Analysis results indicate the effectiveness of the proposed models in that they can describe the behavior of spatial correlation coefficients with higher accuracy.

Beschreibung: Terrestrial laser scanning (TLS) has become a popular tool for acquiring source data points which can be used to construct digital elevation models (DEMs) for a wide number of applications. A TLS point cloud often has a very fine spatial resolution, which can represent well the spatial variation of a terrain surface. However, the uncertainty in DEMs created from this relatively new type of source data is not well understood, which forms the focus of this letter. TLS survey data representing four terrain surfaces of different characteristics were used to explore the effects of surface complexity and typical TLS data density (in terms of data point spacing) on DEM accuracy. The spatial variation in TLS data can be decomposed into parts corresponding to the signal of spatial variation (of terrain surfaces) and noise due to measurement error. We found a linear relation between the DEM error and the typical TLS data spacings considered (30–100 mm) which arises as a function of the interpolation error, and a constant contribution from the propagated data noise. This letter quantifies these components for each of the four surfaces considered and shows that, for the interpolation method considered here, higher density sampling would not be beneficial.

Beschreibung: A novel way to estimate the live fuel moisture content (LFMC) was explored from the ratio of canopy water content (CWC) and foliage dry biomass (FDB). The CWC was estimated using the PROSAIL (PROSPECT + SAIL) radiative transfer model from the Landsat 8 product. A weak constraint 4-D variational data assimilation method was employed to assimilate the temporally estimated leaf area index into a soil-water-atmosphere-plant (SWAP) model for optimizing the model control variables. Then, the SWAP model was reinitialized with this optimum set of control variables, and better prediction of FDB was obtained. Results showed that a high accuracy level was achieved for the estimated CWC ( $R^{2}=0.91$ , $mbox{RMSE}=84.74 mbox{g/m}^2$ ) and FDB ( $R^2=0.88$ , $mbox{RMSE}=48.54 mbox{g/m} ^2$ ) when compared with in situ measured values. However, the accuracy level of estimated LFMC was poor ( $R^2=0.59$ , $mbox{RMSE} =30.85%$ ) . Further analyses find that the estimated LFMC is reliable for low LFMC but challenged for high LFMC, which indicates that the presented method still makes sense to the assessment of wildfire risk since the wildfire generally occurs when the vegetation is in low LFMC condition.

Beschreibung: In this letter, we present the use of experimental human micro-Doppler signature data gathered by a multistatic radar system to discriminate between unarmed and potentially armed personnel walking along different trajectories. Different ways of extracting suitable features from the spectrograms of the micro-Doppler signatures are discussed, particularly empirical features such as Doppler bandwidth, periodicity, and others, and features extracted from singular value decomposition (SVD) vectors. High classification accuracy of armed versus unarmed personnel (between 90% and 97% depending on the walking trajectory of the people) can be achieved with a single SVD-based feature, in comparison with using four empirical features. The impact on classification performance of different aspect angles and the benefit of combining multistatic information is also evaluated in this letter.

Beschreibung: Automatic urban area detection in remote sensing images is an important application in the field of earth observation. Most of the existing methods employ feature classifiers and thereby contain a data training process. Moreover, some methods cannot detect urban areas in complex scenes accurately. This letter proposes an automatic urban area detection method that uses multiple features that have different resolutions. First, a downsampled low-resolution image is used to segment the candidate area. After the corner points of the urban area are extracted, a weighted Gaussian voting matrix technique is employed to integrate the corner points into the candidate area. Then, the edge features and homogeneous region are extracted by using the original high-resolution image. Using these results as the input, the processes of guided filtering and contrast enhancement can finally detect accurately the urban areas. This method combines multiple features, such as corner, edge, and regional characteristics, to detect the urban areas. The experimental results show that the proposed method has better detection accuracy for urban areas than the existing algorithms.

Beschreibung: In marine sciences, time series are often nonlinear and nonstationary. Adequate and specific methods are needed to analyze such series. In this letter, an application of the empirical mode decomposition method (EMD) associated to the Hilbert spectral analysis (HSA) is presented. Furthermore, EMD-based time-dependent intrinsic correlation (TDIC) analysis is applied to consider the correlation between two nonstationary time series. Four temperature time series obtained from automatic measurements in nearshore waters of the Réunion island are considered, recorded every 10 min from July 2011 to January 2012. The application of the EMD on these series and the estimation of their power spectra using the HSA are illustrated. The authors identify low-frequency tidal waves and display the pattern of correlations at different scales and different locations. By TDIC analysis, it was concluded that the high-frequency modes have small correlation, whereas the trends are perfectly correlated.

Beschreibung: Detecting vehicles in aerial images provides important information for traffic management and urban planning. Detecting the cars in the images is challenging due to the relatively small size of the target objects and the complex background in man-made areas. It is particularly challenging if the goal is near-real-time detection, i.e., within few seconds, on large images without any additional information, e.g., road database and accurate target size. We present a method that can detect the vehicles on a 21-MPixel original frame image without accurate scale information within seconds on a laptop single threaded. In addition to the bounding box of the vehicles, we extract also orientation and type (car/truck) information. First, we apply a fast binary detector using integral channel features in a soft-cascade structure. In the next step, we apply a multiclass classifier on the output of the binary detector, which gives the orientation and type of the vehicles. We evaluate our method on a challenging data set of original aerial images over Munich and a data set captured from an unmanned aerial vehicle (UAV).

Beschreibung: We compare five slope correction methods developed by Walter et al. , Montes et al. , Schleppi et al. , España et al. , and Gonsamo et al. (referred to as WAL, MON, SCH, ESP, and GON, respectively) using artificial fisheye pictures simulated by graphics software and a lookup table (LUT) retrieval method. The LUT is built by simulating the directional gap fraction as a function of leaf area index (LAI) and average leaf inclination angle (ALIA) using the Poisson law. LAI and ALIA estimates correspond to the case of the LUT that provides the lowest root-mean-square error between the observed gap fractions after slope correction and the simulated ones. Three LAI values (1.5, 3.5, and 5.5), four ALIA values (26.8°, 45°, 57.5°, and 63.2°), and three slope angles (0°, 20°, and 50°) constituted 36 samples of random scenes. ESP is recommended because its results are accurate and independent on the leaf angle distribution (LAD), while GON only performs well for spherical LAD. The three other methods present less good performances with underestimation or overestimation of LAI and/or ALIA depending on the LAD, and the recommended order for them is MON, SCH, and WAL.

Beschreibung: In this letter, an improved phase correlation (PC) method based on 2-D plane fitting and the maximum kernel density estimator (MKDE) is proposed, which combines the idea of Stone's method and robust estimator MKDE. The proposed PC method first utilizes a vector filter to minimize the noise errors of the phase angle matrix and then unwraps the filtered phase angle matrix by the use of the minimum cost network flow unwrapping algorithm. Afterward, the unwrapped phase angle matrix is robustly fitted via MKDE, and the slope coefficients of the 2-D plane indicate the subpixel shifts between images. The experiments revealed that the improved method can effectively avoid the impact of outliers on the phase angle matrix during the plane fitting and is robust to aliasing and noise. The matching accuracy can reach 1/50th of a pixel using simulated data. The real image sequence tracking experiment was also undertaken to demonstrate the effectiveness of the proposed PC method with a registration accuracy of root-mean-square error better than 0.1 pixels.

Beschreibung: Accurately mapping forest carbon density by combining sample plots and remotely sensed images has become popular because this method provides spatially explicit estimates. However, mixed pixels often impede the improvement of the estimation. In this letter, regression modeling and spectral unmixing analysis were integrated to improve the estimation of forest carbon density for the You County of Hunan, China, using Landsat Thematic Mapper images. Linear spectral unmixing with and without a constraint (LSUWC and LSUWOC) and nonlinear spectral unmixing (NSU) were compared to derive the fractions of five endmembers, particularly forests. Stepwise regression, logistic regression, and polynomial regression (PR) with and without the forest fraction used as an independent variable and the product of the forest fraction image and the map from the best model without the forest fraction were compared. The models were developed using 56 sample plots, and their results were validated using 26 test plots. The decomposition of mixed pixels was assessed using higher spatial resolution SPOT images and a corresponding land cover map. The results showed that 1) LSUWC more accurately estimated the endmember fractions than LSUWOC and NSU, 2) PR had the greatest estimation accuracy of forest carbon, and 3) combining regression modeling and spectral unmixing increased the estimation accuracy by 31%–39%, and introducing the forest fraction into the regressions performed better than the product of forest fraction image and the results from PR without the fraction. This implied that the integrations provided great potential in reducing the impacts of mixed pixels in mapping forest carbon.

Beschreibung: This letter proposes a multiresolution technique to address the high computational cost in remote sensing image registration. The scale-invariant feature transform is applied to detect keypoints and descriptors, and then, global information combined with descriptors is utilized to establish keypoint mappings. Keypoints are first classified according to their octaves. Then, in the lowest resolution, the keypoints of the largest octave are mapped with descriptors and the global information, giving an initial affine transformation $T_0$ . In the next octave, the keypoints of the second largest octave are mapped by employing $T_0$ to narrow the space of matching keypoints. By this means, the process of establishing keypoint correspondences is conducted from one resolution (octave) to the next as the obtained transformation gets finer until we get to the highest resolution. Due to the high computational expense of computing global information, the proposed technique is important for aligning large-size remote sensing imagery. Experimental results show that the proposed method can achieve a comparable registration accuracy but with a less computational cost than directly building keypoint mappings on images of large size.

Beschreibung: Gold nanorods (GNRs) with surface plasmon resonance peak at 1063.8 nm were fabricated and experimentally exploited as the single and combined saturable absorber (SA) in a $Q$ -switched Nd:YAG laser for the first time. In the situation using GNRs as a single SA, the maximum pulse energy of 19 $muhbox{J} $ was achieved at a pulse repetition rate of 20 kHz. However, due to the small effective modulation depth of the GNRs, microsecond pulses were generated in such a GNRs-based passively $Q$ -switched laser. A novel configuration using GNRs as a combined SA in an acoustooptic $Q$ -switched Nd:YAG laser was proposed. The small nonlinear loss modulation induced by the GNRs played a distinctive role in flexibly manipulating the nanosecond pulse waveforms. Our study demonstrated the feasibility of generating high-energy pulses by singly using GNRs-based SA in all solid-state lasers. Meanwhile, a new application mode incorporating GNRs as a combined SA was studied, providing an alternative method to take advantage of the nanomaterial-based SA with a relatively small modulation depth.

Beschreibung: In this letter, we present an efficient parallel implementation of composite kernels in support vector machines (SVMs) for hyperspectral image (HSI) classification. Our implementation makes effective use of commodity graphics processing units (GPUs). Specifically, we port the calculation of composite kernels to GPUs, perform intensive computations based on NVidia's compute unified device architecture, and execute the rest of the operations related with control and small data calculations in the CPU. Our experimental results, conducted using real hyperspectral data sets and NVidia GPU platforms, indicate significant improvements in terms of computational effectiveness, achieving near-real-time performance of spatial–spectral HSI classification for the first time in the literature.

Beschreibung: Compared with airborne laser scanning, terrestrial laser scanning (TLS) offers ground-based point cloud data of trees and provides greater potential to accurately estimate tree and stand parameters. However, there is a lack of effective methods to accurately identify locations of individual trees from TLS point cloud data. It is also unknown whether the estimation accuracy of the parameters, including tree height (H), diameter at breast height (DBH), and so on, using TLS can meet the requirement of forest management and planning. In this letter, a novel method to effectively process point cloud data and further determine the locations of individual trees in a stand based on the central coordinates of point cloud data on a defined grid according to the largest DBH was developed. Moreover, a point-cloud-data-based convex hull algorithm and the cylinder method were, respectively, used to estimate DBH and H of individual trees. This study was conducted in a pure Chinese fir plantation of 45 trees located in Huang-Feng-Qiao forest farm, You County of Hunan, China. The comparison of the estimated and observed values showed that the obtained tree locations had errors of less than 20 cm, and the relative root mean square errors for the estimates of both DBH and H were less than 5%. This implies that TLS is very promising for the retrieval of tree and stand parameters in forest stands. For the applications of these methods to mixed forests with a structure of multilayer canopies, further examination is needed.

Beschreibung: Commercial, industrial, and military aerospace designs are increasingly deploying MEMS micro inertial measurement unit (MIMU) for motion control, automation, and positioning applications, such as the unmanned aerial vehicle (UAV), robot, and smart phone. On the one hand, MIMU has the merit of low cost, small size, low-power consumption, and high shock resistance, but on the other hand, low-cost MIMU is affected by systematic error caused by the instability of the drift, scaling factors, and axes misalignment, which may lead to large errors in the position and attitude’s determination from time to time. That means calibration before use is an effective way to improve the practical precision of MIMU. However, many customers have no precise turntable to calibrate the MIMU before they use it. To address these problems, this paper presents an easy self-calibration method to implement calibration of the MIMU on a common table only with an inclined surface, no precise turntable is needed. The calibration method is based on the following principles. First, the module of the output vector of the orthogonal configured three-axis accelerometers is equal to unit gravity. Second, when IMU rotates to a known gesture with a stable axis, the angles can be calculated through integration. Third, when the accelerometers’ parameters are calculated, it can act as a level datum. Furthermore, the accelerometers on the inclined surface are used to determine the rotating heading datum. Finally, after a series static positions test and rotating test, the parameters can be extracted and estimated. To demonstrate the success and the convenience of the proposed method, comparison experiments with the precision turntable have been made on an ADI’s MIMU. The calibration results show that the accuracy and precision of this method is quite equivalent with the turntable-based calibration, and the scale factors error with an order of magnitude always equal or less than $10^{-5}$ . The observed static and dynamic yaw maximum angular error in a certain period is <0.8°, the pitch maximum angular error is <0.5°, and the roll maximum angular error is <0.3°.

Beschreibung: Time–frequency packing (TFP) transmission provides the highest achievable spectral efficiency with a constrained symbol alphabet and detector complexity. In this paper, the application of the TFP technique to fiber-optic systems is investigated and experimentally demonstrated. The main theoretical aspects, design guidelines, and implementation issues are discussed, focusing on those aspects which are peculiar to TFP systems. In particular, adaptive compensation of propagation impairments, matched filtering, and maximum a posteriori probability detection are obtained by a combination of a two-dimensional equalizer and four eight-state parallel Bahl–Cocke–Jelinek–Raviv (BCJR) detectors. A novel algorithm that ensures adaptive equalization, channel estimation, and a proper distribution of tasks between the equalizer and BCJR detectors is proposed. A set of irregular low-density parity-check codes with different rates is designed to operate at low error rates and approach the spectral efficiency limit achievable by TFP at different signal-to-noise ratios. An experimental demonstration of the designed system is finally provided with five dual-polarization QPSK-modulated optical carriers, densely packed in a 100-GHz bandwidth, employing a recirculating loop to test the performance of the system at different transmission distances.

Beschreibung: We investigated nonlinear optical characteristics of Tungsten disulfide (WS 2 ) films and experimentally demonstrated their high potential for application as nonlinear saturable absorbers in passively mode-locked fiber lasers. Side polished fiber (SPF) was fabricated and WS 2 film was overlaid to provide an efficient evanescent field interaction. The WS 2 film was prepared using two methods: liquid phase exfoliation to form few-layer nano-sheets, and chemical vapor deposition (CVD) to grow uniform multilayer WS 2 on a SiO 2 substrate. Two SPF saturable absorbers were prepared by either spin coating WS 2 solution or lifting off the multilayer CVD WS 2 on SPF. An all-fiber ring cavity was built and the WS 2 film overlaid on SPF was employed as a mode locker along with Er-doped fiber as a gain medium. Using the spin-coated WS 2 SPF, stable soliton-like pulses were generated with a spectral width of 5.6 nm and 467 fs pulse duration. The fiber laser cavity containing CVD WS2 SPF generated a transform-limited soliton pulse train with a spectral width of 8.23 nm and a pulse duration of 332 fs. Our study confirmed a high potential of WS 2 film as a novel 2-D nonlinear optical material for laser applications.