Anomaly detection in hyperspectral imagery has been an active topic among the remote sensing applications. It aims at identifying anomalous targets with different spectra from their surrounding background. Therefore, an effective detector should be able to distinguish the anomalies, especially for the weak ones, from the background and noise. In this article, we propose a novel method for hyperspectral anomaly detection based on total variation (TV) and sparsity regularized decomposition model. This model decomposes the hyperspectral imagery into three components: background, anomaly, and noise. In order to distinguish effectively these components, a union dictionary consisting of both background and potential anomalous atoms is utilized to represent the background and anomalies, respectively. Moreover, the TV and the sparsity-inducing regularizations are incorporated to facilitate the separation. Besides, we present a new strategy for constructing the union dictionary with the density peak-based clustering. The proposed detector is evaluated on both simulated and real hyperspectral data sets and the experimental results demonstrate its superiority compared with several traditional and state-of-the-art anomaly detectors.

中文翻译：

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用于高光谱异常检测的具有联合字典的总变异和稀疏正则化分解模型

高光谱图像中的异常检测一直是遥感应用中的一个活跃话题。它旨在识别具有与其周围背景不同光谱的异常目标。因此，一个有效的检测器应该能够从背景和噪声中区分出异常，尤其是弱异常。在本文中，我们提出了一种基于总变异（TV）和稀疏正则化分解模型的高光谱异常检测新方法。该模型将高光谱图像分解为三个部分：背景、异常和噪声。为了有效区分这些成分，使用由背景和潜在异常原子组成的联合字典分别表示背景和异常。而且，TV 和稀疏诱导正则化被合并以促进分离。此外，我们提出了一种使用基于密度峰值的聚类构建联合字典的新策略。所提出的检测器在模拟和真实的高光谱数据集上进行了评估，实验结果证明了其与几种传统和最先进的异常检测器相比的优越性。