基于端元提取和低秩稀疏矩阵分解的高光谱图像异常目标检测  

Hyperspectral Image Abnormal Target Detection Based on End-Member Extraction and Low-Rank and Sparse Matrix Decomposition

作　　者：杨国亮 龚家仁 习浩 喻丁玲 Yang Guoliang;Gong Jiaren;Xi Hao;Yu Dingling(School of Electrical Engineering and Automation,Jiangaci University of Science and Tech nology,Ganzhou,Jiangaci 341000,China)

机构地区：[1]江西理工大学电气工程与自动化学院,江西赣州341000

出　　处：《激光与光电子学进展》2021年第22期459-468,共10页Laser & Optoelectronics Progress

基　　金：国家自然科学基金(51365017)。

摘　　要：为了抑制高光谱图像(HSI)混合像元和噪声在复杂背景中对异常目标检测的干扰,充分提取和利用HSI的光谱特征和空间特征,提出了一种基于端元提取和低秩稀疏矩阵分解的HSI异常目标检测算法。首先,对原始HSI进行最优分数阶傅里叶变换。然后,采用连续最大角凸锥算法对变换后的HSI进行端元提取,得到端元和相应的丰度矩阵,并通过行约束的低秩稀疏矩阵分解方法将丰度矩阵分解为具有低秩特性的背景分量和具有稀疏特性的异常分量。最后,构建背景协方差矩阵,通过马氏距离检测异常目标。实验结果表明,本算法在HSI异常目标检测中具有很好的检测性能。In this study,to suppress the interference of mixed pixels and noise in hyperspectral images(HSI)on abnormal target detection in a complex background and fully extract and utilize the spectral and spatial features of HSI,a HSI abnormal target detection algorithm based on end-member extraction and low-rank and sparse matrix decomposition is proposed.First,optimal fractional-order Fourier transform is applied to the original HSI.Then,the sequential maximum angle convex cone algorithm is used to extract the endmembers of the transformed HSI;subsequently,the end members and corresponding abundance matrix are obtained.The abundance matrix is decomposed into a low-rank background component and an abnormal component with sparse characteristics using the solution of the low-rank and sparse matrix decomposition method with row constraints.Finally,the background covariance matrix is constructed and abnormal targets are detected using the Mahalanobis distance.Experimental results show that the proposed algorithm exhibits good performance in HSI abnormal target detection.

关 键 词：遥感 高光谱图像 连续最大角凸锥 最优分数阶傅里叶变换 低秩稀疏矩阵分解 异常目标检测 

分 类 号：TP751.2[自动化与计算机技术—检测技术与自动化装置]