From Trained to Untrained: A Novel Change Detection Framework Using Randomly Initialized Models With Spatial–Channel Augmentation for Hyperspectral Images

Abstract

Deep learning approaches have been extensively applied to change detection in hyperspectral images (HSIs). However, the majority of them encounter scarcity of training samples or rely on complex structures and learning strategies. Although untrained change detection models have been proved to be effective in relief above problems, they were constructed using regular convolutions and treated spatial locations and channels equally, which are insufficient to extract discriminative features and lead to limited accuracy. Given this, a novel untrained framework using randomly initialized models with spatial-channel augmentation (RICD) is proposed for HSI change detection in this paper. It consists of two major modules: 1) an enhanced feature extraction network using successive dilation-deformable feature extraction blocks, which can extract multiscale spatial-spectral features over unfixed sampling locations. It enlarges the field of view of convolutions and takes arbitrary neighborhood into consideration, which helps to increase the discriminativeness of the extracted features; 2) a change sensitive feature augmentation and comparison module integrating feature selection and spatial-channel augmentation strategies, which can exploit spatial context and channel importance. It magnifies difference between changed pixels and unchanged ones and emphasizes contribution of significant channels of the selected change sensitive features. Despite that convolution operations are included in RICD, all the weights are untrained and fixed once they are randomly initialized, indicating that the RICD can work in an unsupervised manner. Its performance is tested over three widely used hyperspectral datasets. Quantitative and qualitative comparisons with several state-of-the-art unsupervised methods reveal the effectiveness of the RICD method.