多窗口融合判别子空间的高光谱图像异常检测

doi:10.3969/j.issn.0255-8297.2019.01.007

应用科学学报 ›› 2019, Vol. 37 ›› Issue (1): 64-72.doi: 10.3969/j.issn.0255-8297.2019.01.007

多窗口融合判别子空间的高光谱图像异常检测

马春笑, 黄远程, 胡荣明, 张春森

西安科技大学测绘科学与技术学院, 西安 710054

收稿日期:2018-01-25 修回日期:2018-06-13 出版日期:2019-01-31 发布日期:2019-01-31
通信作者: 黄远程,博士,讲师,研究方向:高光谱遥感、图像处理与模式识别,E-mail:yuancheng.huang@foxmail.com E-mail:yuancheng.huang@foxmail.com
基金资助:
国家自然科学青年基金（No.E0305/1112/JC01）；自然科学青年基金（No.41401306）；大学生创新创业训练计划项目基金（No.201710704032）资助

Discriminant Subspace and Multi-window Fusion RX Algorithm for Hyperspectral Image Anomaly Detection

MA Chun-xiao, HUANG Yuan-cheng, HU Rong-ming, ZHANG Chun-sen

College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China

Received:2018-01-25 Revised:2018-06-13 Online:2019-01-31 Published:2019-01-31

摘要/Abstract

摘要：

由于高光谱图像异常检测受到不规则背景和噪声的干扰，直接应用传统的RX异常检测算法会造成很高的虚警和很大的运算量.针对这一问题，提出了一种基于判别子空间的结合多窗口融合的RX算法.首先在无先验信息的前提下采用聚类的方式得到样本类别，并对占优聚类样本进行判别特征提取；然后利用正交子空间投影使背景和目标信息达到最大程度的分离以实现对背景的抑制，从而在抑制背景的基础上利用局部多窗口融合的RX算法进行异常检测；最后将AUC值作为评价检测方法性能的指标.NUANCE和HYDICE高光谱数据异常目标检测实验的AUC值统计结果表明：多窗口融合算法在检测性能方面优于经典的全局和局部RX算法，它对背景和噪声有更强的抑制作用，且检测到的异常目标精确，可见该算法是有效而可行的.

关键词: RX算法, 判别子空间, 高光谱图像, 多窗口融合

Abstract:

Disturbed by heterogeneous background and noise, the direct application of traditional RX anomaly detection algorithm for hyperspectral image often results in high false alarms. In order to solve this problem, an improved RX algorithm based on discriminant subspace combined with multi-window fusion is proposed. Firstly, the discriminant features of dominant clustering samples are extracted. Secondly, the orthogonality subspace projection which is built by dominant feature vectors is used to obtain the maximum separation of the background and the target information, achieving the suppression of the background. Then multiple RX with different local window size are applied for the anomaly target enhanced data. Finally, the multi-window RX results are added together. The performance on testing methods is evaluated by AUC. The AUC statistical values of the NUANCE and HYDICE hyperspectral data anomaly detection experiments show that the multi-window fusion algorithm outperforms the classical global and local RX algorithms in detection performance, and it has a stronger inhibition on the background and noise, the detected abnormal target is more accurate, which proves the effectivity and feasibility of the proposed algorithm.

Key words: multi-window fusion, hyperspectral image, discriminant subspace, RX algorithm

中图分类号:

TP751

马春笑, 黄远程, 胡荣明, 张春森. 多窗口融合判别子空间的高光谱图像异常检测[J]. 应用科学学报, 2019, 37(1): 64-72.

MA Chun-xiao, HUANG Yuan-cheng, HU Rong-ming, ZHANG Chun-sen. Discriminant Subspace and Multi-window Fusion RX Algorithm for Hyperspectral Image Anomaly Detection[J]. Journal of Applied Sciences, 2019, 37(1): 64-72.

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多窗口融合判别子空间的高光谱图像异常检测

Discriminant Subspace and Multi-window Fusion RX Algorithm for Hyperspectral Image Anomaly Detection

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