首页 > 论文 > 光学学报 > 37卷 > 4期(pp:428001--1)

联合空间预处理与谱聚类的协同稀疏高光谱异常检测

Joint Spatial Preprocessing and Spectral Clustering Based Collaborative Sparsity Anomaly Detection for Hyperspectral Images

  • 摘要
  • 论文信息
  • 参考文献
  • 被引情况
  • PDF全文
分享:

摘要

针对利用稀疏表示进行高光谱图像异常目标检测效率不高的问题,基于高光谱图像成像原理和图像结构,充分利用高光谱图像的空间特性和光谱特性,并在它们之间建立协同处理机制,提出了联合空间预处理与谱聚类的协同稀疏高光谱图像异常目标检测算法。该算法首先对高光谱图像空间特性进行分析,并结合光谱特性进行空间预处理,使得处理后的高光谱图像更易于异常目标的检测;利用建立在谱图划分思想基础上的谱聚类方法进行波段子集划分,谱聚类方法具有收敛于全局最优解、聚类速度快的特点;利用提出的新的空间和光谱协同稀疏差异指数方法对每个子集进行异常目标检测,该协同稀疏方式充分考虑了高光谱图像的空间特性和光谱特性,通过对每个波段子集检测结果进行叠加,得到最终异常检测结果。利用真实的AVIRIS高光谱图像和合成的高光谱图像对算法进行仿真实验和结果分析,结果表明该算法具有稳健性,同时检测精度高,虚警率低。

Abstract

In order to overcome the low efficiency of anomaly detection for hyperspectral images based on sparse representation, a joint spatial preprocessing and spectral clustering based collaborative sparsity anomaly detection algorithm is proposed, which makes full use of the spatial and spectral properties of hyperspectral images, and establishes a cooperative processing mechanism between the spatial and spectral properties, according to the imaging principle and structure of the hyperspectral imagery. The spatial properties of the hyperspectral images are analyzed, and the spatial preprocessing is combined with the spectral properties, which makes the anomalous targets in hyperspectral images easier to be detected. Then, the spectral clustering method based on spectrogram division is used to divide the band subsets, and the spectral clustering method has the features of convergence to the global optimal solution and fast speed. The anomalous targets in each band subset are detected with the proposed new space and spectral collaborative sparsity divergence index method. This collaborative sparsity method considers the spatial and spectral properties of the hyperspectral imagery. Final anomaly detection result is obtained by the superposition of the results of each band subset. The real AVIRIS and synthetic hyperspectral imagery data sets are used for simulations. Simulation results demonstrate that the proposed algorithm is robust, and has higher precision and lower false alarm probability.

投稿润色
补充资料

中图分类号:TP751.1

DOI:10.3788/aos201737.0428001

所属栏目:遥感与传感器

基金项目:国家自然科学基金(61571145)、黑龙江省博士后基金(LBH-Z14062)

收稿日期:2016-10-13

修改稿日期:2016-10-20

网络出版日期:--

作者单位    点击查看

成宝芝:哈尔滨工程大学计算机科学与技术学院, 黑龙江 哈尔滨 150001大庆师范学院机电工程学院, 黑龙江 大庆 163712
赵春晖:哈尔滨工程大学信息与通信工程学院, 黑龙江 哈尔滨 150001
张丽丽:大庆师范学院机电工程学院, 黑龙江 大庆 163712哈尔滨工程大学信息与通信工程学院, 黑龙江 哈尔滨 150001
张健沛:哈尔滨工程大学计算机科学与技术学院, 黑龙江 哈尔滨 150001

联系人作者:成宝芝(chengbaozhigy@163.com)

备注:成宝芝(1976-),男,博士,副教授,主要从事高光谱图像处理方面的研究。

【1】Reed I S, Yu X. Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution[J]. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1990, 38(10): 1760-1770.

【2】Kwon H, Nasrabadi N M. Kernel RX-algorithm: a nonlinear anomaly detector for hyperspectral imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(2): 388-397.

【3】Banerjee A, Burlina P, Diehl C. A support vector method for anomaly detection in hyperspectral imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(8): 2282-2291.

【4】Chen Derong, Gong Jiulu, He Guanglin, et al. A RFS-SVDD algorithm for hyperspectral global anomaly detection[J]. Journal of Astronautics, 2010, 31(1): 228-232.
谌德荣, 宫久路, 何光林, 等. 高光谱图像全局异常检测RFS-SVDD算法[J]. 宇航学报, 2010, 31(1): 228-232.

【5】Chen Y, Nasrabadi N M, Tran T D. Sparse representation for target detection in hyperspectral imagery[J]. IEEE Journal of Selected Topics in Signal Processing, 2011, 5(3): 629-640.

【6】Zhao Chunhui, Li Xiaohui, Zhu Haifeng. Hyperspectral imaging target detection algorithm based on spatial 4 neighborhoods for sparse respresentation[J]. Journal of Harbin Engineering University, 2013, 34(9): 1171-1178.
赵春晖, 李晓慧, 朱海峰. 空间4-邻域稀疏表示的高光谱图像目标检测[J]. 哈尔滨工程大学学报, 2013, 34(9): 1171-1178.

【7】Yuan Z Z, Sun H, Ji K F, et al. Local sparsity divergence for hyperspectral anomaly detection[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(10): 1697-1701.

【8】Xu Y, Wu Z B, Li J, et al. Anomaly detection in hyperspectral images based on low-rank and sparse representation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(4): 1990-2000.

【9】Li J Y, Zhang H Y, Zhang L P, et al. Hyperspectral anomaly detection by the use of background joint sparse representation[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(6): 2523-2533.

【10】Peng Z M, Gurram P, Kwon H, et al. Sparse kernel learning-based feature selection for anomaly detection[J]. IEEE Transactions on Aerospace and Electronic Systems, 2015, 51(3): 1698-1716.

【11】Tang Yidong, Huang Shucai, Ling Qiang, et al. Adaptive kernel collaborative representation anomaly detection for hyperspectral imagery[J]. High Power Laser and Particle Beams, 2015, 27(9): 091008.
唐意东, 黄树彩, 凌 强, 等. 高光谱图像自适应核联合表示异常检测[J]. 强激光与粒子束, 2015, 27(9): 091008.

【12】Huang Yuancheng, Zhong Yanfei, Zhao Yehe, et al. Joint blind unmixing and sparse representation for anomaly detection in hyperspectral image[J]. Geomatics and Information Science of Wuhan University, 2015, 40(9): 1144-1150.
黄远程, 钟燕飞, 赵野鹤, 等. 联合盲分解与稀疏表达的高光谱图像异常目标检测[J]. 武汉大学学报·信息科学版, 2015, 40(9): 1144-1150.

【13】Zhang Lili, Zhao Chunhui, Cheng Baozhi. A joint kernel collaborative representation based approach for anomaly target detection of hyperspectral images[J]. Journal of Optoelectronics·Laser, 2015, 26(11): 2154-2161.
张丽丽, 赵春晖, 成宝芝. 基于联合核协同的高光谱图像异常目标检测[J]. 光电子·激光, 2015, 26(11): 2154-2161.

【14】Zhao Chunhui, Jing Xiaohao, Li Wei. Hyperspectral image target detection algorithm based on StOMP sparse representation[J]. Journal of Harbin Engineering University, 2015, 36(7): 992-996.
赵春晖, 靖晓昊, 李 威. 基于StOMP稀疏方法的高光谱图像目标检测[J]. 哈尔滨工程大学学报, 2015, 36(7): 992-996.

【15】Song Xiangfa, Jiao Licheng. Classification of hyperspectral remote sensing image based on sparse representation and spectral information[J]. Journal of Electronics & Information Technology, 2012, 34(2): 268-272.
宋相法, 焦李成. 基于稀疏表示及光谱信息的高光谱遥感图像分类[J]. 电子与信息学报, 2012, 34(2): 268-272.

【16】Zortea M, Plaza A. Spatial preprocessing for end member extraction[J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(8): 2679-2693.

【17】Luxburg U V. A tutorial on spectral clustering[J]. Statistics and Computing, 2007, 17(4): 395-416.

【18】Cai Xiaoyan, Dai Guanzhong, Yang Libin. Survey on spectral clustering algorithms[J]. Computer Science, 2008, 35(7): 14-18.
蔡晓妍, 戴冠中, 杨黎斌. 谱聚类算法综述[J]. 计算机科学, 2008, 35(7): 14-18.

【19】Zheng Xiumeng, Chen Fucai, Huang Ruiyang. Research on collaborative recommendation algorithms based on parallel spectral clustering[J]. Journal of University of Science and Technology of China, 2016, 46(1): 82-86.
郑修猛, 陈福才, 黄瑞阳. 基于并行化谱聚类的协同推荐算法研究[J]. 中国科学技术大学学报, 2016, 46(1): 82-86.

【20】Chang C I, Jiao X L, Wu C C, et al. Component analysis based unsupervised linear spectral mixture analysis for hyperspectral imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(11): 4123-4137.

【21】Gao G. A parzen-window-kernel-based CFAR algorithm for ship detection in SAR images[J]. IEEE Geoscience and Remote Sensing Letters, 2011, 8(3): 557-561.

【22】Zou J Y, Li W, Du Q. Sparse representation-based nearest neighbor classifiers for hyperspectral imagery[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(12): 2418-2422.

【23】Zhang H Y, Zhai H, Zhang L P, et al. Spectral-spatial sparse subspace clustering for hyperspectral remote sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(6): 3672-3684.

【24】Du B, Zhao R, Zhang L P, et al. A spectral-spatial based local summation anomaly detection method for hyperspectral images[J]. Signal Processing, 2016, 124: 115-131.

【25】Ye Zhen, Bai Lin, Nian Yongjian. Hyperspectral image classification algorithm based on gabor feature and locality-preserving dimensionality reduction[J]. Acta Optica Sinica, 2016, 36(10): 1028003.
叶 珍, 白 璘, 粘永健. 基于Gabor特征与局部保护降维的高光谱图像分类算法[J]. 光学学报, 2016, 36(10): 1028003.

【26】Xue Peng, Wang Zhibin, Zhang Rui, et al. Highly efficient measurement technology based on hyper-spectropolarimetric imaging[J]. Chinese J Lasers, 2016, 43(8): 0811001.
薛 鹏, 王志斌, 张 瑞, 等. 高光谱全偏振成像快捷测量技术研究[J]. 中国激光, 2016, 43(8): 0811001.

引用该论文

Cheng Baozhi,Zhao Chunhui,Zhang Lili,Zhang Jianpei. Joint Spatial Preprocessing and Spectral Clustering Based Collaborative Sparsity Anomaly Detection for Hyperspectral Images[J]. Acta Optica Sinica, 2017, 37(4): 0428001

成宝芝,赵春晖,张丽丽,张健沛. 联合空间预处理与谱聚类的协同稀疏高光谱异常检测[J]. 光学学报, 2017, 37(4): 0428001

您的浏览器不支持PDF插件,请使用最新的(Chrome/Fire Fox等)浏览器.或者您还可以点击此处下载该论文PDF