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Your search keyword '"Zhao, Liaoying"' showing total 30 results
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30 results on '"Zhao, Liaoying"'

3. Hyperspectral Anomaly Detection via MERA Decomposition and Enhanced Total Variation Regularization

Author

Xiao, Qingjiang, Zhao, Liaoying, Chen, Shuhan, and Li, Xiaorun

Abstract

In recent years, tensor representation (TR)-based hyperspectral anomaly detection (HAD) approaches have attracted more and more attention. However, two urgent issues still need to be addressed: 1) existing tensor decomposition approaches for HAD cannot make full use of the spectral–spatial correlation of background components in hyperspectral images (HSIs) and 2) most approaches based on TR overlook the piecewise-smooth of background components that exist simultaneously in the spectral and spatial domains. To this end, with the aid of an advanced multiscale entanglement renormalization ansatz (MERA) tensor network, this article proposes an algorithm based on MERA decomposition and enhanced total variation regularization (MERAETV) for HAD. Specifically, MERA decomposes the background tensor by contracting a top-level factor with the remaining semiorthogonal and orthogonal factors. Due to the intricate interplay between semiorthogonal (low-rank) and orthogonal factors, low-rank MERA approximation exhibits a robust representational capacity that effectively captures the spectral–spatial correlation of the background component. Meanwhile, an enhanced total variation (ETV) regularization is devised to capture the inherent piecewise-smooth of the background component in both spectral and spatial domains. Furthermore, our algorithm incorporates group sparsity constraint and Gaussian noise term to enhance the discrimination between anomalies and background. Finally, a highly efficient update scheme based on the alternating direction method of multipliers (ADMM) is designed. A large number of experiments on one synthetic and seven real HSIs demonstrate the superiority of our proposed approach.

Published
2024
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7. Enhanced Tensor Low-Rank Representation Learning for Hyperspectral Anomaly Detection.

Author

Xiao, Qingjiang, Zhao, Liaoying, Chen, Shuhan, and Li, Xiaorun

Abstract

Nowadays, some tensor-based hyperspectral anomaly detection (HAD) approaches are still insufficient in utilizing the spatial–spectral structure information of hyperspectral images (HSIs), resulting in the inability to isolate the background and abnormal targets well. In this letter, an enhanced tensor low-rank representation (ETLR) learning model is proposed for HAD. Specifically, the original 3-D HSI data is first decomposed into a structural background component, an anomaly component, and a noise component. Among them, with the help of multisubspace learning technology, the structural background component is reformulated by the t-product of the background dictionary tensor and the corresponding coefficient tensor. Then, the tensor nuclear norm (TNN) is adopted to preserve the global low-rank property of the background component in both spatial and spectral dimensions. For the abnormal component, an $\ell _{2,1,1}$ -norm is designed to enhance the group sparsity of abnormal pixels. For the noise component, a tensor $F$ -norm constraint is imposed to suppress the confusion of noise and anomalies. Meanwhile, a robust dictionary tensor that can adequately characterize the background is constructed by using tensor robust principal component analysis (TRPCA). Furthermore, to reduce the interference of redundant information on detection accuracy, the optimal clustering framework (OCF) method is utilized for band selection. Finally, extensive experiments on one simulated and three real HSI datasets confirm that our algorithm is superior to current HAD algorithms. [ABSTRACT FROM AUTHOR]

Published
2023
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