Sea Ice Change Detection in SAR Images Based on Convolutional-Wavelet Neural Networks

Abstract

Sea ice change detection from synthetic aperture radar (SAR) images can be regarded as a classification procedure, in which pixels are classified into changed and unchanged classes. However, existing methods usually suffer from the intrinsic speckle noise of multitemporal SAR images. To solve the problem, this letter presents a change detection method based on convolutional-wavelet neural networks (CWNNs). In CWNN, dual-tree complex wavelet transform is introduced into convolutional neural networks for changed and unchanged pixels' classification, and then, the effect of speckle noise is effectively reduced. In addition, a virtual sample generation scheme is employed to create samples for CWNN training, and the problem of limited samples is alleviated. Experimental results on two real SAR image data sets demonstrate the effectiveness and robustness of the proposed method.