基于双边滤波的最优波段子空间高光谱异常目标检测

成宝芝; 张丽丽

doi:10.3788/YJYXS20193409.0897

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基于双边滤波的最优波段子空间高光谱异常目标检测

图像处理 | 更新时间：2020-07-05

- 基于双边滤波的最优波段子空间高光谱异常目标检测
- Optimal bands subspace anomaly detection for hyperspectral imagery based on bilateral filtering
- 液晶与显示 2019年34卷第9期页码：897-904
- 作者机构：
  
  大庆师范学院机电工程学院, 黑龙江大庆 163712
- 作者简介：
  
  [ "成宝芝(1976—), 男, 吉林东丰人, 博士, 副教授, 2012年于哈尔滨工程大学获得博士学位, 2014年进入哈尔滨工程大学计算机科学与技术博士后流动站, 2017年9月出站, 主要从事高光谱图像处理与大数据分析方面的研究。E-mail:chengbaozhigy@163.com" ]
- 基金信息：
  
  黑龙江省自然科学基金(LH2019F040);Natural Science Foundation of Heilongjiang(LH2019F040)
- DOI：10.3788/YJYXS20193409.0897
  中图分类号： TP751.1
- 收稿日期：2019-04-10，
  
  录用日期：2019-6-4，
  
  纸质出版日期：2019-09-05
- 稿件说明：
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成宝芝, 张丽丽. 基于双边滤波的最优波段子空间高光谱异常目标检测[J]. 液晶与显示, 2019,34(9):897-904.

Bao-zhi CHENG, Li-li ZHANG. Optimal bands subspace anomaly detection for hyperspectral imagery based on bilateral filtering[J]. Chinese journal of liquid crystals and displays, 2019, 34(9): 897-904.
成宝芝, 张丽丽. 基于双边滤波的最优波段子空间高光谱异常目标检测[J]. 液晶与显示, 2019,34(9):897-904. DOI： 10.3788/YJYXS20193409.0897.

Bao-zhi CHENG, Li-li ZHANG. Optimal bands subspace anomaly detection for hyperspectral imagery based on bilateral filtering[J]. Chinese journal of liquid crystals and displays, 2019, 34(9): 897-904. DOI： 10.3788/YJYXS20193409.0897.

摘要

针对KRX方法对高光谱图像进行异常目标检测时存在检测效率低和虚警率高的问题，在充分分析高光谱图像数据特征基础上，本文提出一种最优波段子空间方法的高光谱图像异常目标检测算法。该算法首先利用双边滤波方法对高光谱图像进行全局滤波，充分利用双边滤波的优点，使得高光谱图像背景信息得到抑制；然后采用经典的自动子空间方法对高光谱图像进行波段子集划分；再利用联合偏度-峰度指标，在每个波段子集内选出最优波段；最后利用这些最优波段构成新的波段最优子空间，在此基础上，在最优波段子空间中利用Kernel RX算法进行异常目标检测，从而得到异常检测结果。本文利用真实的高光谱图像进行仿真验证，获得异常目标、检测的虚警数和ROC等检测结果。结果表明，该算法具有鲁棒性强、虚警率低和检测精度高等优点。

Abstract

In order to overcome the low detection accuracy and high false alarm rate of anomaly target detection in hyperspectral image based on Kernel RX anomaly detector

the new hyperspectral image anomaly target detection algorithm for optimal bands subspace is proposed on the basis of fully analyzing the characteristics of hyperspectral image data. First

the bilateral filtering method is used to filter the hyperspectral image

which makes the background information of hyperspectral images be suppressed by full using of the advantages of bilateral filtering. Then

the band subset of hyperspectral image is divided by adopting the classical ASD method

and then the optimal bands are selected by using the combined skewness-kurtosis index in each band subset. Finally

the Kernel RX algorithm is used to detect anomaly targets by using these optimal bands. The real AVIRIS hyperspectral imagery data sets are used in the experiments

and the detection results are obtained by anomaly targets

false alarm number and ROC curve. The results show that the proposed algorithm has strong robustness and lower false alarm probability.

关键词

Keywords

references

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.

ZHAO R, DU B, ZHANG L P. Hyperspectral anomaly detection via a sparsity score estimation framework[J]. IEEE Transactions on Geoscience and Remote Sensing , 2017, 55(6): 3208-3222.

IMANI M. RX anomaly detector with rectified background[J]. IEEE Geoscience and Remote Sensing Letters , 2017, 14(8): 1313-1317.

CHANG C I, CHIANG S S. Anomaly detection and classification for hyperspectral imagery[J]. IEEE Transactions on Geoscience and Remote Sensing , 2002, 40(6): 1314-1325.

GUO Q D, ZHANG B, RAN Q, et al . Weighted-RXD and linear filter-based RXD: improving background statistics estimation for anomaly detection in hyperspectral imagery[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2014, 7(6): 2351-2366.

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.

SCHAUM A P. Hyperspectral anomaly detection beyond RX[J]. SPIE, 2007, 6565: 656502.

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.

ZHAO C H, LI J W, MENG M L, et al . A weighted spatial-spectral kernel RX Algorithm and efficient implementation on GPUs[J]. Sensors , 2017, 17(3): 441.

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.

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.

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.

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.

ZORTEA M, PLAZA A. Spatial preprocessing for endmember extraction[J]. IEEE Transactions on Geoscience and Remote Sensing , 2009, 47(8): 2679-2693.

TOMASI C, MANDUCHI R. Bilateral filtering for gray and color images[C]//Proceedings of the 6th International Conference on Computer Vision. Bombay, India: IEEE, 1998: 839-846.

ZHANG Y, ZHANG J P, JIN M. Adaptive subspace decomposition and classification for hyperspectral images[J]. Chinese Journal of Electronics , 2000, 9(1): 82-88.

郭雷, 常威威, 付朝阳.高光谱图像融合最佳波段选择方法[J].宇航学报, 2011, 32(2):374-379.

GUO L, CHANG W W, FU C Y. Band selection of optimal for hyperspectral image fusion[J]. Journal of Astronautics , 2011, 32(2): 374-379. (in Chinese)

王琪, 杨桄, 张俭峰, 等.基于局部联合偏度-峰度的高光谱图像波段选择方法[J].激光与光电子学进展, 2017, 54(11):111004.

WANG Q, YANG G, ZHANG J F, et al . Band selection based on local joint skewness and kurtosis for hyperspectral image[J]. Laser & Optoelectronics Progress , 2017, 54(11): 111004. (in Chinese)

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.

IMANI M. Hyperspectral anomaly detection using differential image[J]. IET Image Processing , 2018, 12(5): 801-809.

ZHAO R, DU B, ZHANG L P, 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.

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