Hyperspectral Anomaly Detection Fused Unified Nonconvex Tensor Ring Factors Regularization

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 nonconvex regularizer, induced by tensor singular value decomposition (TSVD), to simultaneously encode the low-rankness and sparsity of the 3-D gradient TR factors into a unique concise form. The above characterization scheme enables the interpretable gradient TR factors to inherit the low-rankness and smoothness of the original background. To further enhance anomaly detection, we design a generalized nonconvex regularization term to exploit the group sparsity of the anomaly component. To solve the resulting doubly nonconvex model, we develop a highly efficient optimization algorithm based on the alternating direction method of multipliers (ADMM) framework. Experimental results on several benchmark datasets demonstrate that our proposed method outperforms existing state-of-the-art (SOTA) approaches in terms of detection accuracy.