Hyperspectral Target Detection Based on Tensor Sparse Representation

Abstract

The sparse representation-based detection (SRD) algorithm has already shown the effectiveness for hyperspectral target detection (TD) recently. However, SRD does not utilize spatial information of hyperspectral imagery (HSI). In this letter, a novel tensor SRD (TSRD) algorithm is proposed to take jointly the spatial and the spectral information of HSI into account. TSRD extends each atom of both the target and the background dictionaries into a third-order tensor where the local spatial neighborhood information can be well preserved. It not only possesses the advantages of SRD that no assumptions about the target and background distributions are required and spectral variability can be considered but also exploits the spatial information of HSI to further increase the accuracy of TD. The experimental results on both synthetic and real hyperspectral data show that the proposed TSRD method outperforms traditional and state-of-the-art TD methods in terms of detection accuracy.