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Anomaly Detection of Hyperspectral Image by Coarse-to-Fine Tensor Two-Level Decomposition | IEEE Journals & Magazine | IEEE Xplore

Anomaly Detection of Hyperspectral Image by Coarse-to-Fine Tensor Two-Level Decomposition


Abstract:

The high spectral resolution of the hyperspectral image (HSI) has facilitated the wide applications of anomaly detection techniques. However, existing HSI anomaly detecti...Show More

Abstract:

The high spectral resolution of the hyperspectral image (HSI) has facilitated the wide applications of anomaly detection techniques. However, existing HSI anomaly detection (HAD) methods usually deal with HSIs as a 2-D matrix and violate their inherent 3-D structures. Also, the limited spatial resolution often leads to intricate background-anomaly mixed subpixels, which makes accurate background learning and anomaly detection a challenge. To address the issues, this letter proposes to fully explore the inherent 3-D structural properties of HSIs and develop a coarse-to-fine tensor two-level decomposition (CTTD) method for HAD. Specifically, the primary decomposition is performed via tensor robust principal component analysis (TRPCA), to simultaneously discover the global tensor background component and the coarse group sparse anomaly component of HSIs. Then, the secondary decomposition with joint \ell _{2,1,1} and weighted nuclear norms (WNNs) is devised with the obtained global tensor background component in primary decomposition, to pursue the refined 3-D spatial-spectral sparse anomaly component. Finally, with the coarse-to-fine background learning and anomaly detection mechanism, the informative anomaly cues from the coarse group sparse and refined 3-D spatial-spectral sparse anomaly components are enhanced by weighted fusion, to achieve the final accurate detection result. Extensive experiments on various real-world HSI datasets verify the superior performance of our proposed CTTD than some state-of-the-art HAD methods.
Published in: IEEE Geoscience and Remote Sensing Letters ( Volume: 22)
Article Sequence Number: 5501105
Date of Publication: 12 December 2024

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