A Semi-Supervised Multiscale Convolutional Sparse Coding-Guided Deep Interpretable Network for Hyperspectral Image Change Detection

Deep learning (DL) has increasingly become the mainstream technology for hyperspectral image change detection (HSI-CD). However, these methods lack transparency and often overlook priors in hyperspectral images (HSIs), making it difficult to extract more generalizable features directly from them. Moreover, the performance of DL-based methods typically depends heavily on a large corpus of high-quality labeled data, which is often impractical and expensive in real-world scenarios, particularly for complex HSIs. To address these issues, we propose a semi-supervised deep interpretable network for HSI-CD. Specifically, by applying structured sparse prior constraints, we propose a multiscale convolutional sparse coding (MSCSC) model to capture shared and private sparse coefficients (SSC and PSCs) across different scales, extracting multiscale features while reducing redundancy. We then unfold the proposed MSCSC model to establish an MSCSC-guided deep interpretable network (MSCSCNet) that serves as the encoder, namely, MSCSCNet, in which each network module is model-driven, enhancing the transparency of internal mechanisms and extracting more fundamental features. In addition, we devise a two-stage semi-supervised training strategy for MSCSCNet using limited labeled data, combining a change-sensitive teacher-student self-distillation (CS-TSSD) paradigm with a novel loss function to reduce the annotation dependency and remain sensitive to change components. The proposed method not only enhances the transparency of multiscale feature extraction but also accomplishes semi-supervised learning to extract meaningful representations, effectively integrating the advantages of model-driven and data-driven approaches. Comparative experiments on benchmark datasets demonstrate the effectiveness of our method over the existing approaches. Code is available at https://github.com/Jiahuiqu/MSCSCNet