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In this research, a novel robust change detection approach is presented forimbalanced multi-temporal synthetic aperture radar (SAR) image based on deeplearning. Our main contribution is to develop a novel method for generatingdifference image and a parallel fuzzy c-means (FCM) clustering method. The mainsteps of our proposed approach are as follows: 1) Inspired by convolution andpooling in deep learning, a deep difference image (DDI) is obtained based onparameterized pooling leading to better speckle suppression and featureenhancement than traditional difference images. 2) Two different parameterSigmoid nonlinear mapping are applied to the DDI to get two mapped DDIs.Parallel FCM are utilized on these two mapped DDIs to obtain three types ofpseudo-label pixels, namely, changed pixels, unchanged pixels, and intermediatepixels. 3) A PCANet with support vector machine (SVM) are trained to classifyintermediate pixels to be changed or unchanged. Three imbalanced multi-temporalSAR image sets are used for change detection experiments. The experimentalresults demonstrate that the proposed approach is effective and robust forimbalanced SAR data, and achieve up to 99.52% change detection accuracysuperior to most state-of-the-art methods.

A Robust Imbalanced SAR Image Change Detection Approach Based on Deep Difference Image and PCANet