Change Detection for Hyperspectral Images Via Convolutional Sparse Analysis and Temporal Spectral Unmixing

With the increase in the availability of multitemporal hyperspectral images (HSIs), HSIs change detection (CD) methods, including pixel-level and subpixel-level based methods, have attracted great attention in recent years. However, the widespread presence of mixed pixels in HSIs may make it difficult for pixel-level methods to detect subtle changes; meanwhile, the less utilization of spatial information may also lead to limitations in some subpixel-level methods. Therefore, a joint framework, which aims to combine the advantages of pixel-level in spatial utilization and subpixel-level in temporal and spectral exploration, is proposed to enhance the performance of HSIs CD. Two models, convolutional sparse analysis and temporal spectral unmixing, are introduced and presented to characterize different spatial structures and overcome the effects of spectral variability under this framework, respectively. In addition, a multiple CD-based on subpixel analysis is discussed as well. Experiments conducted on three bitemporal HSIs datasets indicate that the proposed framework is robust in capturing effective features and has achieved great detection accuracy.