Polarimetric SAR data feature selection using measures of mutual information

Several algorithms for polarimetric synthetic aperture radar (PolSAR) data indexing and classification were proposed in the state of the art literature. In particular, one of them computes powerful, compact feature descriptors composed of the first three logarithmic cumulants of the BiQuaternion Fractional Fourier Transform (BiQFrFT) coefficients of PolSAR patches. Since the BiQFrFT of each patch is computed at three different angles, the algorithm´s result consists in nine complex-valued features (18 real-valued features) for single polarization images and in nine biquaternion-valued features (72 real-valued features) for fully polarimetric images. In this paper feature selection based on mutual information (MI) is employed to optimally select a subset of features, in order to improve the indexing performances and to minimize the classification error. The improved results are shown on two polarimetric images: a L-band PALSAR image over Danube´s Delta, Romania and a C-band RadarSAT2 image over Brâila, Romania.