基于空谱联合聚类的改进核协同高光谱异常检测

马世欣; 刘春桐; 李洪才; 何祯鑫; 王浩

doi:doi:10.3788/gzxb20194801.0110003

光子学报, 2019, 48 (1): 0110003, 网络出版: 2019-01-27

基于空谱联合聚类的改进核协同高光谱异常检测

Improved Collaborative Algorithm Based on Spatial-spectral Joint Clustering for Hyperspectral Anomaly Detection

论文大纲

马世欣 ^*刘春桐李洪才何祯鑫王浩

作者单位

火箭军工程大学导弹工程学院, 西安 710025

摘要

针对空谱信息中普遍存在的异常干扰现象, 提出了基于空谱联合聚类的自适应核协同表示高光谱异常目标探测算法.算法充分发挥了基于密度的聚类算子(Density-Based Spatial Clustering of Applications with Noise, DBSCAN)对于异常点的筛选特性, 在DBSCAN聚类去除异常波谱的基础上, 采用分波段子集随机投影变换对数据降维处理, 以减少谱噪声和谱冗余, 并采用DBSCAN聚类消除了局部背景像元中的杂乱点对协同探测算法结果的干扰.研究了背景离散度对核参选择的影响, 比较了不同的核估计方法, 并提出基于平均差的自适应核协同算法.采用该方法对AVIRIS和ROSIS的三组数据进行仿真实验并与现有算法进行了对比, 结果表明该算法表现出较好的探测性能.

Abstract

An adaptive-kernel collaborative representation method based on spatial-spectral joint clustering for hyperspectral anomaly detection is proposed, which is well used to solve the abnormal interference in space-spectrum information. The algorithm gives full play to the filtering characteristic of Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for outliers, and is applied to space-spectrum processing. On the basis of removing abnormal spectrum by DBSCAN clustering, random projection for reserved subsets is used to reduce the dimension of the data, so spectral noise and spectral redundancy can be solved properly. Considering the influence of background outliers on collaborative representation detection algorithm, DBSCAN clustering is used to remove the clutter points in the local background pixels. Furthermore, the influence of background dispersion on the selection of kernel parameters is studied. By comparing different kernel estimation methods, an adaptive kernel measure method based on average difference is proposed. The proposed algorithm is used to simulate three sets of AVIRIS and ROSIS data and compared with the international mainstream anomaly detection algorithm, the results show that the proposed algorithm has a good detection performance.

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马世欣, 刘春桐, 李洪才, 何祯鑫, 王浩. 基于空谱联合聚类的改进核协同高光谱异常检测[J]. 光子学报, 2019, 48(1): 0110003. MA Shi-xin, LIU Chun-tong, LI Hong-cai, HE Zhen-xin, WANG Hao. Improved Collaborative Algorithm Based on Spatial-spectral Joint Clustering for Hyperspectral Anomaly Detection[J]. ACTA PHOTONICA SINICA, 2019, 48(1): 0110003.

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