Dual Collaborative Sparse and Total Variation Regularization for Unmixing-Based Change Detection

Hyperspectral change detection is critical for analyzing the temporal evolution of the feature components in multi-temporal hyperspectral images. However, existing methods often fall short of fully exploiting the spatio-temporal-spectral correlations within these images, thereby limiting their accuracy and robustness. This paper introduces a novel hyperspectral change detection method, termed dual collaborative sparse unmixing via variable splitting augmented Lagrangian and total variation (DCLSUnSAL-TV). By integrating dual collaborative sparsity and total variation regularizers, this method capitalizes on the local similarity of changes in the feature components, leveraging the low-rank property of hyperspectral difference images (HSDIs) and their inherent spatial-spectral correlations. A customized abundance-wise-truncation and ensemble strategy is designed to obtain the change map by aggregating the subpixel-level changes with respect to each endmember. Comprehensive comparison and ablation experiments demonstrate the effectiveness of the proposed method in improving the accuracy of change detection. The source code is available at https://github.com/2alsbz/DCLSUnSAL_TV.