  In this research, a novel robust change detection approach is presented for
imbalanced multi-temporal synthetic aperture radar (SAR) image based on deep
learning. Our main contribution is to develop a novel method for generating
difference image and a parallel fuzzy c-means (FCM) clustering method. The main
steps of our proposed approach are as follows: 1) Inspired by convolution and
pooling in deep learning, a deep difference image (DDI) is obtained based on
parameterized pooling leading to better speckle suppression and feature
enhancement than traditional difference images. 2) Two different parameter
Sigmoid 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 of
pseudo-label pixels, namely, changed pixels, unchanged pixels, and intermediate
pixels. 3) A PCANet with support vector machine (SVM) are trained to classify
intermediate pixels to be changed or unchanged. Three imbalanced multi-temporal
SAR image sets are used for change detection experiments. The experimental
results demonstrate that the proposed approach is effective and robust for
imbalanced SAR data, and achieve up to 99.52% change detection accuracy
superior to most state-of-the-art methods.

Computer Vision and Pattern Recognition;Image and Video Processing