A Coarse-to-Fine Hyperspectral Target Detection Method Based on Low-Rank Tensor Decomposition

To solve the problem of low target detection accuracy caused by the related quantities such as background, target, and noise contained in hyperspectral images (HSIs), considering the use of the spatial spectrum and spectral characteristics while increasing the degree of discrimination between target and background, a coarse-to-fine HSI target detection algorithm based on low-rank tensor decomposition (HTDLTD) is proposed. The HTD based on low-rank sparse decomposition mainly decomposes HSIs in spectral dimension, which does not make full use of the spatial information of HSIs, resulting in low detection accuracy. In order to solve this problem, in view of the fact that the hyperspectral third-order tensor can describe the spatial information and spectral information of HSIs equally, the HTD method based on low-rank tensor decomposition (LRTD) is proposed to extract pure background information. Then, in order to solve the problem of low detection accuracy in the case of low target and background discrimination, the rough target detection method based on max over (SMF-MAX) target detection method is proposed to perform rough detection on the original HSI to obtain rough detection results. Finally, in order to further improve the performance of target detection, the fine target detection method based on spectral distance is proposed. By calculating the spectral distance between the original HSI and the synthesized HSI, the final reconstructed target detection result is obtained. Experimental results on three datasets show that the proposed HTDLTD exceeds eight state-of-the-art target detection methods used for comparison.