Wavelet-Inspired Sparse Learning Network for Hyperspectral Image Change Detection

In this letter, a novel wavelet-inspired sparse learning network (WISLNet) is proposed for hyperspectral image change detection (HSI-CD), which introduces wavelet convolution transform into a data-driven low-rank and sparse representation (LRSR) model to build a deep unfolding network in the frequency domain. The WISLNet mainly involves the following key steps. First, the difference image (DI) of the bi-temporal hyperspectral images (Bi-HSIs) is obtained through pixel-by-pixel and band-by-band subtraction operations. Then, the LRSR model is designed as a deep unfolding network with modules for low-rank learning, sparse learning, and DI reconstruction to capture deep semantics related to change identification in the DI. Meanwhile, wavelet convolution is embedded in the above modules to progressively mine the low-rank and sparse features of the DI in the frequency domain. Next, an iterative optimization scheme based on low-rank and sparse features is used to capture the change semantic details between Bi-HSIs. Finally, a wavelet convolution filter is designed and applied to the sparse components to reflect the image change information. Experiments on River and Farmland Bi-HSIs demonstrated that the proposed WISLNet method is able to achieve superior CD results compared to well-known and state-of-the-art deep networks. The code is available at: https://github.com/chengle-zhou/WISLNet.