[1]成宝芝,赵春晖.联合空间预处理与双边滤波的稀疏RX高光谱异常检测[J].哈尔滨工程大学学报,2019,40(04):851-857.[doi:10.11990/jheu.201802031]
 CHENG Baozhi,ZHAO Chunhui.Joint spatial preprocessing and bilateral filtering of sparsity RX anomaly detection for hyperspectral imagery[J].hebgcdxxb,2019,40(04):851-857.[doi:10.11990/jheu.201802031]
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《哈尔滨工程大学学报》[ISSN:1006-6977/CN:61-1281/TN]

卷:
40
期数:
2019年04期
页码:
851-857
栏目:
出版日期:
2019-04-05

文章信息/Info

Title:
Joint spatial preprocessing and bilateral filtering of sparsity RX anomaly detection for hyperspectral imagery
作者:
成宝芝1 赵春晖2
1. 大庆师范学院 机电工程学院, 黑龙江 大庆 163712;
2. 哈尔滨工程大学 信息与通信工程学院, 黑龙江 哈尔滨 150001
Author(s):
CHENG Baozhi1 ZHAO Chunhui2
1. College of Mechanical and Electrical Engineering, Daqing Normal University, Daqing 163712, China;
2. College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
关键词:
高光谱遥感图像异常检测稀疏表示空间预处理双边滤波背景数据高斯滤波核函数
分类号:
TP751.1
DOI:
10.11990/jheu.201802031
文献标志码:
A
摘要:
针对RX异常目标检测算法对高光谱图像异常目标检测精度低和虚警率高的问题,本文提出一种充分利用高光谱图像的空间信息和光谱信息,并联合高光谱图像自身存在的稀疏特性,对经典RX异常检测算法进行改进,得到一种稀疏RX异常目标检测算法。通过利用空间预处理方法抑制背景数据信息,使异常目标点突出,然后利用双边滤波方法再次对高光谱图像进行滤波处理,滤除噪声干扰对高光谱图像的影响;在此基础上,利用稀疏表示理论,计算高光谱图像的稀疏差异指数,再利用稀疏差异指数重构一个高光谱图像数据向量,最后利用RX方法进行异常目标检测,得到异常目标检测结果。利用高光谱图像进行仿真验证,能够得到算法的检测精度高、虚警率低和鲁棒性好。

参考文献/References:

[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] MOLERO J M, PAZ A, GARZÓN E M, et al. Fast anomaly detection in hyperspectral images with RX method on heterogeneous Clusters[J]. The journal of supercomputing, 2011, 58(3):411-419.
[3] KWON H, NASRABAD 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.
[4] KHAZAI S, MOJARADI B. A modified kernel-RX algorithm for anomaly detection in hyperspectral images[J]. Arabian journal of geosciences, 2015, 8(3):1487-1495.
[5] ZHOU Jin, KWAN C, AYHAN B, et al. A novel cluster kernel RX algorithm for anomaly and change detection using hyperspectral images[J]. IEEE transactions on geoscience and remote sensing, 2016, 54(11):6497-6504.
[6] CHEN Yi, 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.
[7] YUAN Zongze, SUN Hao, JI Kefeng, et al. Local sparsity divergence for hyperspectral anomaly detection[J]. IEEE geoscience and remote sensing letters, 2014, 11(10):1697-1701.
[8] LI Jiayi, ZHANG Hongyan, ZHANG Liangpei, 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.
[9] YUAN Yuan, ZHENG Xiangtao, LU Xiaoqiang. Discovering diverse subset for unsupervised hyperspectral band selection[J]. IEEE transactions on image processing, 2017, 26(1):51-64.
[10] ZHAO Rui, DU Bo, ZHANG Liangpei. Hyperspectral anomaly detection via a sparsity score estimation framework[J]. IEEE transactions on geoscience and remote sensing, 2017, 55(6):3208-3222.
[11] ZHAO Rui, DU Bo, ZHANG Liangpei, et al. A robust background regression based score estimation algorithm for hyperspectral anomaly detection[J]. ISPRS journal of photogrammetry and remote sensing, 2016, 122:126-144.
[12] ZORTEA M, PLAZA A. Spatial preprocessing for endmember extraction[J]. IEEE transactions on geoscience and remote sensing, 2009, 47(8):2679-2693.
[13] TOMASI C, MANDUCHI R. Bilateral filtering for gray and color images[C]//Proceedings of the Sixth International Conference on Computer Vision. Bombay, India, India, 1998:839-846.
[14] 廖建尚, 王立国, 郝思媛. 基于双边滤波和空间邻域信息的高光谱图像分类方法[J]. 农业机械学报, 2017, 48(8):140-146, 211.LIAO Jianshang, WANG Liguo, HAO Siyuan. Hyperspectral image classification method combined with bilateral filtering and pixel neighborhood information[J]. Transactions of the Chinese society for agricultural machinery, 2017, 48(8):140-146, 211.
[15] XU Yang, WU Zebin, LI Jun, 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.
[16] ACITON, DIANI M, CORSINI G. On the CFAR property of the RX algorithm in the presence of signal-dependent noise in hyperspectral images[J]. IEEE transactions on geoscience and remote sensing, 2013, 51(6):3475-3491.
[17] 成宝芝, 赵春晖, 张丽丽, 等. 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.CHENG Baozhi, ZHAO Chunhui, ZHANG Lili, et al. Joint spatial preprocessing and spectral clustering based collaborative sparsity anomaly detection for hyperspectral images[J]. Acta optica sinica, 2017, 37(4):0428001.
[18] 谷延锋, 刘颖, 贾友华, 等. 基于光谱解译的高光谱图像奇异检测算法[J]. 红