An Improved Low Rank and Sparse Matrix Decomposition-Based Anomaly Target Detection Algorithm for Hyperspectral Imagery

• Main Authors: Yan Zhang, Yanguo Fan, Mingming Xu, Wei Li, Guangyu Zhang, Li Liu, Dingfeng Yu
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• Subjects: Anomaly target detection; hyperspectral imagery (HSI); low rank; matrix decomposition; parts-based; sparseness
• Online Access: https://ieeexplore.ieee.org/document/9103230/

Anomaly target detection has been a hotspot of the hyperspectral imagery (HSI) processing in recent decades. One of the key research points in the HSI anomaly detection is the accurate descriptions of the background and anomaly targets. Considering this point, we propose a novel anomaly target detector in this article. Improving upon the low-rank and sparse matrix decomposition (LRaSMD) approach, the proposed method assumes that the low-rank component can be described as the parts-based representation. Parts refer to the various ground objects in HSI. A new update rule of the low-rank component and sparse component is proposed. The proposed approach can be divided into three main steps: first, further refining the low-rank component in the LRaSMD model as the parts-based representation. Then, the HSI is decomposed as three parts: the product of the basis matrix and coefficient matrix, sparse matrix, and noise. Second, the basis vectors matrix, coefficient matrix, and sparse matrix are solved by the new update rules. Third, since the anomaly targets exist in the sparse matrix, the sparse matrix is thus employed to detect the anomaly targets. The experiments implemented for five data sets demonstrate that the proposed algorithm achieved a better performance than the traditional algorithms.