Change detection for hyperspectral images based on tensor analysis

Change detection for multitemporal hyperspectral images (HSIs) involves two major steps: change feature extraction and classification. For the first part, conventional methods mostly consider spectral features but neglect spatial patterns. Since multitemporal HSIs consist of four dimensions (one for time, one for spectral domain and two for spatial domain), we propose using 4-dimensional Higher Order Singular Value Decomposition (4D-HOSVD) based on tensor algebra to capture the details in all the dimensions simultaneously and thus producing comprehensive change features. To emphasize on the effectiveness of the change feature extraction method, this paper reduces the change classification to a simple binary problem: a pixel is either changed or unchanged. Experimental results show that 4D-HOSVD can outperform its matrix counterpart, Principal Component Analysis (PCA), as well as some other widely adopted method.