High Resolution SAR Classification using Rényi Entropy Constrained Spectrum Estimates

This paper discusses a feature extraction procedure based on the estimation of spectral components of the complex SAR image for scene classification. The estimation is performed in an iterative manner, using the periodogram of SAR image patches of fixed size. A Rényi Entropy constraint is introduced in the process of selection of best spectral components for content characterization. Results are presented on a database consisting of 1650 image patches covering urban areas. Results show that by selecting the spectral components that maximize the Rényi entropy of the spectrum with ??=3 can improve the accuracy of the classification.