ALBERT

Description: The use of hyperspectral data to determine the abundance of constituents in a certain portion of the Earth's surface relies on the capability of imaging spectrometers to provide a large amount of information at each pixel of a certain scene. Today, hyperspectral imaging sensors are capable of generating unprecedented volumes of radiometric data. The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), for example, routinely produces image cubes with 224 spectral bands. This undoubtedly opens a wide range of new possibilities, but the analysis of such a massive amount of information is not an easy task. In fact, most of the existing algorithms devoted to analyzing multispectral images are not applicable in the hyperspectral domain, because of the size and high dimensionality of the images. The application of neural networks to perform unsupervised classification of hyperspectral data has been tested by several authors and also by us in some previous work. We have also focused on analyzing the intrinsic capability of neural networks to parallelize the whole hyperspectral unmixing process. The results shown in this work indicate that neural network models are able to find clusters of closely related hyperspectral signatures, and thus can be used as a powerful tool to achieve the desired classification. The present work discusses the possibility of using a Self Organizing neural network to perform unsupervised classification of hyperspectral images. In sections 3 and 4, the topology of the proposed neural network and the training algorithm are respectively described. Section 5 provides the results we have obtained after applying the proposed methodology to real hyperspectral data, described in section 2. Different parameters in the learning stage have been modified in order to obtain a detailed description of their influence on the final results. Finally, in section 6 we provide the conclusions at which we have arrived.

Description: During the last several years, a number of airborne and satellite hyperspectral sensors have been developed or improved for remote sensing applications. Imaging spectrometry allows the detection of materials, objects and regions in a particular scene with a high degree of accuracy. Hyperspectral data typically consist of hundreds of thousands of spectra, so the analysis of this information is a key issue. Mathematical morphology theory is a widely used nonlinear technique for image analysis and pattern recognition. Although it is especially well suited to segment binary or grayscale images with irregular and complex shapes, its application in the classification/segmentation of multispectral or hyperspectral images has been quite rare. In this paper, we discuss a new completely automated methodology to find endmembers in the hyperspectral data cube using mathematical morphology. The extension of classic morphology to the hyperspectral domain allows us to integrate spectral and spatial information in the analysis process. In Section 3, some basic concepts about mathematical morphology and the technical details of our algorithm are provided. In Section 4, the accuracy of the proposed method is tested by its application to real hyperspectral data obtained from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) imaging spectrometer. Some details about these data and reference results, obtained by well-known endmember extraction techniques, are provided in Section 2. Finally, in Section 5 we expose the main conclusions at which we have arrived.

Description: Two fixed-threshold Canada Centre for Remote Sensing and European Space Agency (CCRS and ESA) and three contextual GIGLIO, International Geosphere and Biosphere Project, and Moderate Resolution Imaging Spectroradiometer (GIGLIO, IGBP, and MODIS) algorithms were used for fire detection with Advanced Very High Resolution Radiometer (AVHRR) data acquired over Canada during the 1995 fire season. The CCRS algorithm was developed for the boreal ecosystem, while the other four are for global application. The MODIS algorithm, although developed specifically for use with the MODIS sensor data, was applied to AVHRR in this study for comparative purposes. Fire detection accuracy assessment for the algorithms was based on comparisons with available 1995 burned area ground survey maps covering five Canadian provinces. Overall accuracy estimations in terms of omission (CCRS=46%, ESA=81%, GIGLIO=75%, IGBP=51%, MODIS=81%) and commission (CCRS=0.35%, ESA=0.08%, GIGLIO=0.56%, IGBP=0.75%, MODIS=0.08%) errors over forested areas revealed large differences in performance between the algorithms, with no relevance to type (fixed-threshold or contextual). CCRS performed best in detecting real forest fires, with the least omission error, while ESA and MODIS produced the highest omission error, probably because of their relatively high threshold values designed for global application. The commission error values appear small because the area of pixels falsely identified by each algorithm was expressed as a ratio of the vast unburned forest area. More detailed study shows that most commission errors in all the algorithms were incurred in nonforest agricultural areas, especially on days with very high surface temperatures. The advantage of the high thresholds in ESA and MODIS was that they incurred the least commission errors.

Description: For many Earth and Space Science applications, automatic geo-registration at sub-pixel accuracy has become a necessity. In this work, we are focusing on building an operational system, which will provide a sub-pixel accuracy registration of Landsat-5 and Landsat-7 data. The input to our registration method consists of scenes that have been geometrically and radiometrically corrected. Such pre-processed scenes are then geo-registered relative to a database of Landsat chips. The method assumes a transformation composed of a rotation and a translation, and utilizes rotation- and translation-invariant wavelets to extract image features that are matched using statistically robust feature matching and a generalized Hausdorff distance metric. The registration process is described and results on four Landsat input scenes of the Washington, D.C. area are presented.

Description: The MODIS sensor, launched on NASA's Terra satellite at the end of 1999, was designed with 36 spectral channels for a wide array of land, ocean, and atmospheric investigations. MODIS has a unique ability to observe fires, smoke, and burn scars globally. Its main fire detection channels saturate at high brightness temperatures: 500 K at 4 microns and 400 K at 11 microns, which can only be attained in rare circumstances at the I kin fire detection spatial resolution. Thus, unlike other polar orbiting satellite sensors with similar thermal and spatial resolutions, but much lower saturation temperatures (e.g. AVHRR and ATSR), MODIS can distinguish between low intensity ground surface fires and high intensity crown forest fires. Smoke column concentration over land is for the first time being derived from the MOMS solar channels, extending from 0.41 microns to 2.1 microns. The smoke product has been provisionally validated both globally and regionally over southern Africa and central and south America. Burn scars are observed from MODIS even in the presence of smoke, using the 1.2 to 2.1 micron channels. MODIS burned area information is used to estimate pyrogenic emissions. A wide range of these fire and related products and validation are demonstrated for the wild fires that occurred in northwestern United States in the summer of 2000. The MODIS rapid response system and direct broadcast capability is being developed to enable users to obtain and generate data in near real time. It is expected that health and land management organizations will use these systems for monitoring the occurrence of fires and the dispersion of smoke within two to six hours after data acquisition.

Description: Seasonal snow cover in South America was examined in this study using passive microwave satellite data from the Special Sensor Microwave Imagers (SSM/I) on board Defense Meteorological Satellite Program (DMSP) satellites. For the period from 1992-1998, both snow cover extent and snow depth (snow mass) were investigated during the winter months (May-August) in the Patagonia region of Argentina. Since above normal temperatures in this region are typically above freezing, the coldest winter month was found to be not only the month having the most extensive snow cover but also the month having the deepest snows. For the seven-year period of this study, the average snow cover extent (May-August) was about 0.46 million sq km and the average monthly snow mass was about 1.18 x 10(exp 13) kg. July 1992 was the month having the greatest snow extent (nearly 0.8 million sq km) and snow mass (approximately 2.6 x 10(exp 13) kg).

Description: Time-series of surface elevation change, which are constructed from 7-years (1992-1999) of ERS-1 and 2 satellite radar altimeter data of Antarctica, show significant seasonal, inter-annual, and long-term changes. Elevation time-series are created from altimeter crossovers among 90-day data periods on a 50 km grid to 81.5 degrees S and fit with a multivariate linear/sinusoidal function to give the average rate of elevation change (dH/dt) and account for seasonal changes. On the major Ronne, Filchner, and Ronne ice shelves, the dH/dt are small or near zero. In contrast, the ice shelves of the Antarctic Peninsula and along the West Antarctic coast appear to be thinning significantly, with a 23 +/- 3 cm a(exp -1) surface elevation decrease on the Larsen ice shelf and a 65 +/- 4 cm a(exp -1) decrease on the Dotson ice shelf. Significant elevation decreases are obtained over most of the drainage basins of the Pine Island and Thwaites glaciers. Significant increases are obtained over most of the other grounded ice in Marie Byrd Land, the Antarctic Peninsula, and Coates Land. Over the sector from 85 degrees W to 115 degrees W, which includes the Pine Island and Thwaites basins, the average elevation is significantly decreasing by 8.1 cm a(exp -1). The corresponding ice thickness change is about -11 cm a(exp -1), with a corresponding mass loss of 82 Gt a(exp -1), and a 0.22 mm a(exp -1) contribution to global sea level rise. In terms of elevation change, the decrease in the Pine Island-Thwaites sector is largely balanced by the increase in the Marie Byrd Land, but only balanced by about 1/4 in terms of ice thickness change and contribution to sea level rise. The overall average elevation change for the grounded ice is + 1.2 cm a(exp -1). Using an average bedrock uplift of 2.5 cm a(exp -1), implies an average ice thickness decrease of 1.3 cm a(exp -1), a mass loss of 22 Gt a(exp -1), and a 0.06 mm a(exp -1) contribution to global sea level rise.

Description: The MODerate resolution Imaging Spectroradiometer (MODIS) algorithm for determining aerosol characteristics over ocean is performing with remarkable accuracy. A two-month data set of MODIS retrievals co-located with observations from the AErosol RObotic NETwork (AERONET) ground-based sunphotometer network provides the necessary validation. Spectral radiation measured by MODIS (in the range 550 - 2100 nm) is used to retrieve the aerosol optical thickness, effective particle radius and ratio between the submicron and micron size particles. MODIS-retrieved aerosol optical thickness at 660 nm and 870 nm fall within the expected uncertainty, with the ensemble average at 660 nm differing by only 2% from the AERONET observations and having virtually no offset. MODIS retrievals of aerosol effective radius agree with AERONET retrievals to within +/- 0.10 micrometers, while MODIS-derived ratios between large and small mode aerosol show definite correlation with ratios derived from AERONET data.

Description: The need for accurate geometric and radiometric information over large areas has become increasingly important. Laser altimetry is one of the key technologies for obtaining this geometric information. However, there are important application areas where the observing platform has its orbit constrained by the other instruments it is carrying, and so the spatial resolution that can be recorded by the laser altimeter is limited. In this paper we show how information recorded by one of the other instruments commonly carried, a high-resolution imaging camera, can be combined with the laser altimeter measurements to give a high resolution estimate both of the surface geometry and its reflectance properties. This estimate has an accuracy unavailable from other interpolation methods. We present the results from combining synthetic laser altimeter measurements on a coarse grid with images generated from a surface model to re-create the surface model.

Description: Aerosol optical depths are derived operationally for the first time over land in the visible wavelengths by MODIS (Moderate Resolution Imaging Spectroradiometer) onboard the EOSTerra spacecraft. More than 300 Sun photometer data points from more than 30 AERONET (Aerosol Robotic Network) sites globally were used in validating the aerosol optical depths obtained during July - September 2000. Excellent agreement is found with retrieval errors within (Delta)tau=+/- 0.05 +/- 0.20 tau, as predicted, over (partially) vegetated surfaces, consistent with pre-launch theoretical analysis and aircraft field experiments. In coastal and semi-arid regions larger errors are caused predominantly by the uncertainty in evaluating the surface reflectance. The excellent fit was achieved despite the ongoing improvements in instrument characterization and calibration. This results show that MODIS-derived aerosol optical depths can be used quantitatively in many applications with cautions for residual clouds, snow/ice, and water contamination.