Hyperspectral anomalous change detection based on joint sparse representation

Anomalous change detection aims at finding small but unusual changes from the unchanged or generally changed background in multi-temporal hyperspectral remote sensing images. It is important to model the spectral variations of background so as to highlight the anomalous changes. In this paper, we proposed a hyperspectral anomalous change detection method based on joint sparse representation. A background dictionary is constructed by the randomly selected pixels in the stacked multi-temporal images. The local neighborhood pixels surrounding the test pixel are presented by joint sparse representation with the background dictionary. Thus, the change tendencies in the local background are modeled by the active dictionary bases. The difference of separate reconstruction coefficients of the test pixel with the active bases will reflect the probability to be anomalously changed. Three detectors, which are coefficient difference, Mahalanobis distance of coefficient difference and multi-temporal residual analysis, are proposed to measure the change intensity. Two experiments with the datasets of “Viareggio 2013 Trial” and one Hyperion indicate that the proposed method obtains better performances than the comparative methods.