Chemical plume detection in hyperspectral imagery via joint sparse representation

In this paper, we propose a new spatial-temporal joint sparsity method for the identification and detection of chemical plume in hyperspectral imagery. The proposed algorithm relies on two key observations: 1. each hyperspectral pixel can be approximately represented by a sparse linear combination of the training samples; and 2. neighborhood pixels from the same hyperspectral image as well as consecutive hyperspectral frames usually have similar spectral characteristics. By grouping these pixels into a joint group structure and forcing them to have the same sparsity support of the training samples, we effectively exclude the correlation of not only spatial but also time domain of the HSI data. Before the presence of this paper, almost no methods have made use of the temporal information for the detection of chemical plume in hyperspectral video data. Furthermore, the proposed method shows very competitive results with the Adaptive Matched Subspace Detector (AMSD) algorithm where the chemical types are predefined.