Hyperspectral Time-Series Target Detection Based on Spectral Perception and Spatial–Temporal Tensor Decomposition

The detection of camouflaged targets in the complex background is a hot topic of current research. Existing hyperspectral target detection algorithms do not take advantage of spatial information and rarely use temporal information. It is difficult to obtain the required targets, and the detection performance in hyperspectral sequences with complex background will be low. Therefore, a hyperspectral time-series target detection method based on spectral perception and spatial-temporal tensor decomposition (SPSTT) is proposed. Firstly, a sparse target perception strategy based on spectral matching is proposed. To initially acquire the sparse targets, the matching results are adjusted by using the correlation mean of the prior spectrum, the pixel to be measured and the four-neighborhood pixel spectra. The separation of target and background is enhanced by making full use of local spatial structure information through local topology graph representation of the pixel to be measured. Secondly, in order to obtain a more accurate rank and make full use of temporal continuity and spatial correlation, a spatial-temporal tensor model based on the Gamma norm and L _2,1 norm is constructed. Furthermore, an excellent alternating direction method of multipliers is proposed to solve this model. Finally, spectral matching is fused with spatial-temporal tensor decomposition in order to reduce false alarms and retain more right targets. A 176-band hyperspectral image sequence (BIT-HSIS-I) dataset is collected for the hyperspectral target detection task. It is found by testing on the collected dataset that the proposed SPSTT has superior performance over the state-of-the-art algorithms.