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The radio echo sounding (RES) system is one of the most widely used active remote sensing techniques for polar ice sheet exploration, including bedrock morphology studies and subglacial lake investigations. Recently, bedrock characterization has been improved through the analysis of radar echo strength. In this paper, the analysis of the RES signal amplitude has been used to collect information about the controversial problem of electromagnetic ice absorption to highlight areas of high reflectivity variation, ascribable to wet ice-bedrock interfaces. A method to distinguish a wet or dry bedrock-ice interface using a model to describe the internal ice absorption is proposed and discussed. Moreover, the comparison between the ice absorption rates from RES measurements and from European Project for Ice Coring in Antarctica Dome C (Antarctica) ice core conductivity data, the signal amplitude contributions of internal ice layers, and different kinds of rock interface is evaluated. Encouraged by the results, the data analysis led to obtaining a bedrock reflectivity variation map of the Dome C area. This map outlined a wide dispersion of wet/dry rock interfaces in the studied area, indicating the possibility of flowing water along both sides of the Concordia Trench. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of modeling high-resolution synthetic aperture radar clutter data from different vegetated areas. We analyzed moving and stationary target recognition (MSTAR) data sets focusing on histograms, moments, and covariance of clutter amplitude, texture, and speckle. The most celebrated statistical models are tested on real data of grass field or wood and trees to validate the goodness of fit of the compound Gaussian model in different scenarios. The results demonstrate that for grass fields, the compound Gaussian model provides a good data fitting. This is not the case for woods images where the speckle is not more Gaussian distributed. Covariance analysis and concluding remarks complete this paper. [ABSTRACT FROM AUTHOR]

Developments in the field of image understanding in remote sensing over the past four decades are reviewed, with an emphasis, initially, on the contributions of David Landgrebe and his colleagues at the Laboratory for Applications of Remote Sensing, Purdue University. The differences in approach required for multispectral, hyperspectral and radar image data are emphasised, culminating with a commentary on methods commonly adopted for multisource image analysis. The treatment concludes by examining the requirements of an operational multisource thematic mapping process, in which it is suggested that the most practical approach is to analyze each data type separately, by techniques optimized to that data's characteristics, and then to fuse at the label level. [ABSTRACT FROM AUTHOR]