Integration of the k-nearest neighbours and patch-based features for PolSAR image classification by using a two-branch residual network

ABSTRACT

A two-branch residual deep learning network is proposed for polarimetric synthetic aperture radar (PolSAR) image classification in this work. The proposed method integrates local information of pixels from a rectangular region (patch-based features) with global information of the k-nearest neighbours (k-NN) constituting a deformable shape. Both contextual and polarimetric information are utilized to find the nearest neighbours. The experiments on two real Airborne Synthetic Aperture Radar (AIRSAR) datasets show superior performance of the proposed network with a relatively small training set.

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Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.