Hyperspectral Anomaly Detection Fused Unified Nonconvex Tensor Ring Factors Regularization
Wenjin Qin, Hailin Wang, Hao Shu, Feng Zhang, Jianjun Wang, Xiangyong Cao, Xi-Le Zhao, and Gemine Vivone

TL;DR
This paper introduces HAD-EUNTRFR, a novel hyperspectral anomaly detection method that leverages nonconvex tensor ring regularization to better capture background correlations and improve detection accuracy.
Contribution
It proposes a unified nonconvex tensor ring regularizer and an efficient optimization algorithm to enhance hyperspectral anomaly detection performance.
Findings
Outperforms state-of-the-art methods in detection accuracy
Effectively captures spatial-spectral correlations in HSIs
Enhances background modeling with low-rank and smoothness constraints
Abstract
In recent years, tensor decomposition-based approaches for hyperspectral anomaly detection (HAD) have gained significant attention in the field of remote sensing. However, existing methods often fail to fully leverage both the global correlations and local smoothness of the background components in hyperspectral images (HSIs), which exist in both the spectral and spatial domains. This limitation results in suboptimal detection performance. To mitigate this critical issue, we put forward a novel HAD method named HAD-EUNTRFR, which incorporates an enhanced unified nonconvex tensor ring (TR) factors regularization. In the HAD-EUNTRFR framework, the raw HSIs are first decomposed into background and anomaly components. The TR decomposition is then employed to capture the spatial-spectral correlations within the background component. Additionally, we introduce a unified and efficient…
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