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Hyperspectral images exhibit strong dependencies across spatial and spectral neighbors, which have been proved to be very useful for hyperspectral image classification. The recently defined conditional random field (CRF) can effectively model and use the dependencies for classification of hyperspectral images in a unified probabilistic framework. However, in order to be computationally tractable, the usual CRFs are limited to incorporate only pairwise potentials. Thus, the usual CRFs can capture only pairwise interactions and neglect higher order dependencies, which are potentially useful high-level properties particularly for the classification of hyperspectral image consisting of complex components. This paper overcomes this limitation by developing hyperspectral image classification algorithm based on a CRF with sparse higher order potentials, which are specially designed to incorporate complex characteristics of hyperspectral images. To efficiently implement the CRF model at training step, this paper develops an efficient local method under the piecewise training framework, while at inference step, this proposes a simple strategy to combine the piecewisely trained model to overcome the possible over-counting problems. Moreover, the combined model with the specially defined potentials can be efficiently inferred by graph cut method. Experiments on the real-world data attest to the accuracy, effectiveness, and efficiency of the proposed model on modeling and classifying hyperspectral images. [ABSTRACT FROM AUTHOR]

This paper addresses the remote sensing image pan-sharpening problem from the perspective of compressed sensing (CS) theory which ensures that with the sparsity regularization, a compressible signal can be correctly recovered from the global linear sampled data. First, the degradation model from a high- to low-resolution multispectral (MS) image and high-resolution panchromatic (PAN) image is constructed as a linear sampling process which is formulated as a matrix. Then, the model matrix is considered as the measurement matrix in CS, so pan-sharpening is converted into signal restoration problem with sparsity regularization. Finally, the basis pursuit (BP) algorithm is used to resolve the restoration problem, which can recover the high-resolution MS image effectively. The QuickBird and IKONOS satellite images are used to test the proposed method. The experimental results show that the proposed method can well preserve spectral and spatial details of the source images. The pan-sharpened high-resolution MS image by the proposed method is competitive or even superior to those images fused by other well-known methods. [ABSTRACT FROM AUTHOR]

In this paper, heterogeneous clutter models are used to describe polarimetric synthetic aperture radar (PolSAR) data. The KummerU distribution is introduced to model the PolSAR clutter. Then, a detailed analysis is carried out to evaluate the potential of this new multivariate distribution. It is implemented in a hierarchical maximum likelihood segmentation algorithm. The segmentation results are shown on both synthetic and high-resolution PolSAR data at the X- and L-bands. Finally, some methods are examined to determine automatically the “optimal” number of segments in the final partition. [ABSTRACT FROM AUTHOR]

The linear mixture model is a convenient way to describe image pixels as a linear combination of pure spectra – termed endmembers. The fractional contribution from each endmember is calculated through inversion of the linear model. Despite the simplicity of the model, a nonnegativity constraint that is imposed on the fractions leads to an unmixing problem for which it is hard to find a closed analytical solution. Current solutions to this problem involve iterative algorithms, which are computationally intensive and not appropriate for unmixing large number of pixels. This paper presents an algorithm to build fuzzy membership functions that are equivalent to the least square solution of the fully constrained linear spectral unmixing problem. The efficiency and effectiveness of the proposed solution is demonstrated using both simulated and real data. [ABSTRACT FROM AUTHOR]

At the core of the technical methodology for creating the Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER) global digital elevation model (GDEM) is the procedure for generating a global set of 1 ^\circ latitude-by-1^ \circ longitude tiles containing DEM data in geographic latitude and longitude coordinates and with one arc second postings from scene-based ASTER DEMs. The ASTER GDEM is comprised of all tiles, which include at least 0.01% land in them, each containing 3601-by-3601 elevation data points. The tiles are created by stacking all observed scene DEM data matched geographically to the tile container, selecting valid data for each pixel, removing abnormal data values, and then averaging the remaining selected valid data to assign as the tile elevation data. Valid Earth surface elevation values typically clump within a \pm40-m range and are assumed to be lower in elevation than residual cloud outliers. The filtering process, which assigns the tile elevation data, is one of the most important parts of the GDEM generation system. The median-based selection method is designed to efficiently select the valid data for each pixel. The combination of cloud-masked and non-cloud-masked data is another important part of the process to assign accurate elevation data for each pixel, because the cloud masking capability is not perfect. The algorithm used to combine both data is described. The postprocessing for inland water bodies is successfully carried out to yield a flattened elevation value. This postprocessing is essential to assign unique elevation values for each inland water body. GDEM tile elevation data include some residual anomalies, mostly in areas with fewer than three valid stacked input scenes. The correction method using existing reference data also is described. [ABSTRACT FROM PUBLISHER]