Reconstruction-Based 2DPCANet for Unsupervised SAR Image Change Detection

Abstract

In this letter, considering the effectiveness of 2-D principal component analysis (2DPCA) on the exploration of local spatial relationships, a reconstruction-based 2DPCA (Rec-2DPCA) operation was designed for feature extraction and injected into the architecture of PCANet for change detection of bitemporal synthetic aperture radar (SAR) image. Specifically, as the projection of an image patch on one eigenvector computed by 2DPCA breaks the one-to-one relationship between feature map and eigenvalue, we adopted Rec-2DPCA at various network layers and developed two variants of PCANet, namely, 2DPCANet and (2-D + 1-D)PCANet. In the experiments, using three real SAR image datasets, we analyzed the performance of all comparison methods, and our proposals achieved a more appealing performance than other methods.