Li Lu, Li Wei, Qu Ying, Zhao Chunhui, Tao Ran, Du Qian
IEEE Trans Neural Netw Learn Syst. 2022 Mar;33(3):1037-1050. doi: 10.1109/TNNLS.2020.3038659. Epub 2022 Feb 28.
The key to hyperspectral anomaly detection is to effectively distinguish anomalies from the background, especially in the case that background is complex and anomalies are weak. Hyperspectral imagery (HSI) as an image-spectrum merging cube data can be intrinsically represented as a third-order tensor that integrates spectral information and spatial information. In this article, a prior-based tensor approximation (PTA) is proposed for hyperspectral anomaly detection, in which HSI is decomposed into a background tensor and an anomaly tensor. In the background tensor, a low-rank prior is incorporated into spectral dimension by truncated nuclear norm regularization, and a piecewise-smooth prior on spatial dimension can be embedded by a linear total variation-norm regularization. For anomaly tensor, it is unfolded along spectral dimension coupled with spatial group sparse prior that can be represented by the l -norm regularization. In the designed method, all the priors are integrated into a unified convex framework, and the anomalies can be finally determined by the anomaly tensor. Experimental results validated on several real hyperspectral data sets demonstrate that the proposed algorithm outperforms some state-of-the-art anomaly detection methods.
高光谱异常检测的关键在于有效地将异常与背景区分开来，尤其是在背景复杂且异常微弱的情况下。高光谱图像（HSI）作为一种图像 - 光谱融合的立方体数据，本质上可以表示为一个整合了光谱信息和空间信息的三阶张量。在本文中，提出了一种基于先验的张量近似（PTA）用于高光谱异常检测，其中HSI被分解为一个背景张量和一个异常张量。在背景张量中，通过截断核范数正则化将低秩先验纳入光谱维度，并且可以通过线性全变差范数正则化在空间维度嵌入分段光滑先验。对于异常张量，沿着光谱维度展开并结合空间组稀疏先验，该先验可以由l - 范数正则化表示。在所设计的方法中，所有先验都被整合到一个统一的凸框架中，并且最终可以通过异常张量确定异常。在几个真实高光谱数据集上验证的实验结果表明，所提出的算法优于一些现有的异常检测方法。
