Evaluation of polarimetric features for sea ice characterization at X, C and L- band SAR - Singha, Suman (2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017)
In recent years SAR Polarimetry has become a valuable tool in space-borne SAR based sea ice analysis. This work compares the polarimetric backscatter behavior of sea ice in space-borne X-band C-band and L-band Synthetic Aperture Radar (SAR) imagery. Two sets of spatially and temporally near coincident fully polarimetric acquisitions from the TerraSAR-X/TanDEM-X, RADARSAT-2 and ALOS-2 satellites are investigated. Our algorithmic approach for an automated sea ice classification consists of two steps. In the first step, we perform a polarimetric feature extraction procedure. The resulting feature vectors are then ingested into a trained neural network classifier to arrive at a pixel-wise supervised classification. Based on the common coherency and covariance matrix, we extract a number of features and analyze the relevance and redundancy by means of mutual information for the purpose of sea ice classification. Coherency matrix based features which require an eigendecomposition are found to be either of low relevance or redundant to other covariance matrix based features, which makes coherency matrix based features dispensable for the purpose of sea ice classification. Among the most useful features for classification are matrix invariant based features (Geometric Intensity, Scattering Diversity, Surface Scattering Fraction). This analysis reveals analogous results for all four acquisitions, in both X-band and C-band frequencies and slightly different for L-band. The subsequent classification produces similarly promising results for all four acquisitions. In particular, the overlapping image portions exhibit a reasonable congruence of detected.