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The characterization of ground targets from a remotely sensed Synthetic Aperture Radar (SAR) image is addressed by polarimetric decomposition. The polarimetric SAR (PolSAR) decomposition measures the contribution of total backscatter from different scattering mechanisms using SAR images. The ambiguities present in the retrieval of scattering are the major problems associated with the model-based decomposition which could be reduced with a combination of interferometric coherence and PolSAR backscatter. The objective of this study is to improve the polarimetric decomposition model for identifying the scattering mechanisms based on the Polarimetric SAR Interferometry (PolInSAR) coherence for natural and manmade features. In this paper, we have proposed a model-based polarimetric decomposition using PolInSAR decorrelation. The PolInSAR decorrelation is exploited here to distinguish the time-varying and invariant scatterers present in the ground. The volume scattering power was calculated using the proposed decorrelation parameter which is the combination of PolInSAR coherence and decorrelation. The proposed algorithm has been tested on spaceborne multifrequency SAR datasets consisting of X-band TerraSAR-X and TerraSAR-X add-on for Digital Elevation Measurement (TanDEM-X), C-band Radarsat-2, and phased array L-band synthetic aperture radar-2 (PALSAR-2) data of advanced land observing satellite-2 (ALOS-2) PolInSAR data for the Dehradun region, India. The results show that there is a remarkable reduction in the ambiguities present in the identification of the scattering mechanism from the SAR image by using the proposed decorrelation-based decomposition model. Moreover, the algorithm is tested on X-band TerraSAR-X and TanDEM-X data of the Haridwar area and Rudrapur area, Uttarakhand, India to analyse the potential of the proposed decomposition technique in representing different manmade and natural features. [ABSTRACT FROM AUTHOR]

In this paper we present the results of our analyses of multidiurnal low-resolution Moderate Resolution Imaging Spectroradiometer (MODIS) thermal data for coal fire-related thermal anomaly detection. Results are presented for data of the Jharia coal mining region of India. We combine three relatively new approaches: first, we use low-resolution MODIS data for coal fire area analyses, which has only been undertaken by a few authors. Second, we analyse data from four different times of day (morning, afternoon, evening and predawn) and for three different bands (MODIS bands 20, 32 and a ratio thereof); and third, we use an unbiased automated algorithm for thermal anomaly extraction of local thermal anomalies. The MODIS data analysed stem from the years 2001 and 2005. In 2001, MODIS data were only been available from the platform TERRA as morning and evening data (around 1030 and 2200 h). In 2005, MODIS data were available from this platform as well as from the platform AQUA as afternoon and predawn data (around 0130 and 0200 h). Our analyses indicate that MODIS multidiurnal data, and especially bands 20, 32 and ratio bands thereof, have a high potential for the detection of coal fire zones and coal fire hot spot zones, as well as for regular thermal monitoring activities. However, the data are not suitable for a quantitative coal fire analysis concerning fire outline, fire temperature or fire classification into surface and subsurface fires. We used higher-resolution ASTER and LANDSAT data from 2005 and 2002 for general orientation and later comparison of thermal anomaly extraction results. We also used high-resolution Quickbird data for the characterization of individual anomalous thermal clusters. Comparisons demonstrate that even low-resolution thermal sensors such as MODIS can support coal fire detection and zonation into warm and very hot zones. [ABSTRACT FROM AUTHOR]