A non-local sparse unmixing based hyperspectral change detection with unsupervised deep clustering

Abstract

Hyperspectral images (HSIs) are now widely utilized in change detection (CD) tasks because of their rich spectral signatures. As for detecting and discriminating fine spectral change between different types, hyperspectral unmixing (HU) methods investigate changes into a subpixel-level so as to distinguish the endmember within each pixel. However, current HU models cannot directly utilize the correlation difference information between temporal HSIs during unmixing. This paper proposes a hyperspectral sparse unmixing CD model, which directly extracts the changed endmembers of the difference matrix and uses their abundance to represent the change information. To improve the unmixing accuracy, a non-local mean strategy has been integrated into the HU model, incorporating the non-local spatial information of HSIs. But this comes at the cost of increased computational demands. To further expedite non-local sparse unmixing, we apply an unsupervised deep clustering for homogeneous region segmentation to reduce the search space of non-local mean regularizer, where pixels in the same region possess spectral similarity. A split&merge strategy is employed to infer the number of homogeneous regions. For the generated abundance maps of each endmember, we adopt a self-adaptive abundance truncation strategy to search the optimal threshold for accumulating abundance matrix and retaining the changed regions. Finally, both the experimental results and theoretical analysis confirm the robustness, potential, and validity of our method across multiple HSI datasets.