Tensor based dimension reduction for polarimetric SAR data

With the development of target decomposition theorems for polarimetric synthetic aperture radar (PolSAR) data, various informative polarimetric descriptors could be obtained. The redundancy among these descriptors poses a hindrance to accurate classification. In this paper, we propose a tensor-based dimension reduction technique, which aims to obtain a lower-dimensional intrinsic feature set from the high-dimensional polarimetric manifold. We combine 48 polarimetric features together and formulate them as a third-mode tensor. The spatial information is taken into consideration for feature reduction. Experimental results in comparison with principal component analysis (PCA), independent component analysis (ICA) and Laplacian Eigenmaps (LE) proves its effectiveness.