Gas Plume Detection in Hyperspectral Video Sequence Using Tensor Nuclear Norm

Gas plume detection (GPD) of Hyperspectral video sequences (HVSs) has become a hot topic in the field of remote sensing. The traditional HVS processing methods reshape the extracted video to a 2-D matrix, which is at expense of destroying spatial or spectral structure. In this paper, we propose a novel method of Multi-feature Tensor Decomposition (MTD), where the 3-dimensional (3-D) structure of the extracted video can be seen as a 3-order tensor, thus the spatial and temporal structures in HVS are preserved. We employ the tensor nuclear norm to model the low-rank property of the background, and apply tensor sparse norm to constrain the sparsity of the gas plume. Moreover, taking into consideration the continuity in both spatial and temporal domain of the gas plume, we add a 3-D total variation regularization (3DTV) in the proposed detection model, and assume the support of the gas plume in different features are the same. The final objective function of gas plume detection is efficiently solved by augmented Lagrangian multiplier algorithm (ADMM). Experimental results demonstrate the effectiveness and high detection accuracy of the proposed method.