基于选择性分段行-列二维主成分分析的高光谱图像异常检测

杨桄; 向英杰; 王琪; 田张男

doi:doi:10.3788/lop55.081002

激光与光电子学进展, 2018, 55 (8): 081002, 网络出版: 2018-08-13

基于选择性分段行-列二维主成分分析的高光谱图像异常检测下载： 511次

Anomaly Detection Based on Selective Segmentation Row-Column Two-Dimensional Principal Component Analysis for Hyperspectral Images

论文大纲

杨桄 ¹向英杰 ²王琪 ³田张男 ¹

作者单位

¹ 空军航空大学航空作战勤务学院, 吉林长春 130022

² 中国人民解放军78102部队, 四川成都 610031

³ 中国人民解放军93116部队, 辽宁沈阳 110100

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摘要

高光谱图像具有越来越高的空间和光谱分辨率，其带来了数据量大、相关性强和冗余度高的问题，使得异常检测结果精度不高。为了选择更加有利于异常检测的图像，运用二维主成分分析（2DPCA）方法降维，并引入局部联合偏度-峰度指数进行图像选择，提出了一种基于选择性分段2DPCA的高光谱图像异常检测方法。首先利用相关系数对原始图像进行分段，然后通过旋转数据结构在每个波段子空间中实现行-列二维主成分降维；再选择合适大小的窗口，遍历每个降维结果的主成分，计算窗口内的局部联合偏度-峰度指数，并以此为指标选择用于异常检测的图像。实验结果表明，所提方法的接收机工作特性（ROC）曲线、曲线下面积(AUC)值和Bhattacharyya距离值均优于其他传统的方法，因此具有更好的检测性能。

Abstract

Hyperspectral image have higher and higher spatial and spectral resolution, resulting in a large amount of data, strong correlation and high redundancy, which makes the low accuracy of anomaly detection result. In order to select the image which is more favorable for anomaly detection, we use the two-dimensional principal component analysis (2DPCA) method to reduce the dimension, and introduce the local joint skewness-kurtosis index to image selection. A method based on selective segmentation 2DPCA for hyperspectral image anomaly detection is proposed. Firstly, the original image is segmented by the correlation coefficient, and then the row-column two-dimensional principal component dimension reduction is realized in each band subspace by rotating the data structure. Then, we select an appropriate size window to traverse all the principal components of each dimension reduction result. Meanwhile, the local joint skewness-kurtosis index is calculated in this window, which is regard as an indicator to select the image for anomaly detection. The experimental result shows that the receiver operating characteristic (ROC) curve, the area under the curve (AUC) value and Bhattacharyya distance value of the proposed method are better than other traditional methods, so that it has a better detection performance.

参考文献

[1] 张兵. 高光谱图像处理与信息提取前沿［J］. 遥感学报, 2016, 20(5): 1062-1090.

Zhang B. Advances of hyperspectral image processing and information extraction［J］. Journal of Remote Sensing, 2016, 20(5): 1062-1090.

[2] Tong Q,Xue Y, Zhang L. Progress in hyperspectral remote sensing science and technology in China over the past three decades［J］. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(1): 70-91.

[3] 普晗哗, 王斌, 张立明. 基于流形学习的新高光谱图像降维算法［J］. 红外与激光工程, 2014, 43(1): 232-237.

Pu H Y, Wang B, Zhang L M. New dimensionality reduction algorithms for hyperspectral imagery based on manifold learning［J］. Infrared and Laser Engineering, 2014, 43(1): 232-237.

[4] Yang J, Zhang D, Frangi A F, et al. Two-dimensional PCA: a new approach to appearance-based face representation and recognition［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(1): 131-137.

[5] 周杨, 厉小润, 赵辽英. 改进的高光谱图像线性预测波段选择算法［J］. 光学学报, 2013, 33(8): 0828002.

Zhou Y, Li X R, Zhao L Y. Modified linear-prediction based band selection for hyperspectral image［J］. Acta Optica Sinica, 2013, 33(8): 0828002.

[6] 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 Z Y. Band selection of optimal for hyperspectral image fusion［J］. Journal of Astronautics, 2011, 32(2): 374-379.

[7] 赵春晖, 胡春梅, 石红. 采用选择性分段PCA算法的高光谱图像异常检测［J］. 哈尔滨工程大学学报, 2011, 32(1): 109-113.

Zhao C H, Hu C M, Shi H. Anomaly detection for a hyperspectral image by using a selective section principal component analysis algorithm［J］. Journal of Harbin Engineering University, 2011, 32(1): 109-113.

[8] Kang X D, Li S T, Jon A B. Feature extraction of hyperspectral images with image fusion and recursive filtering［J］. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(6): 3742-3752.

[9] Stefanou M S, Kerekes J P. A method for assessing spectral image utility［J］. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(6): 1698-1706.

[10] Yuan Y, Wang Q, Zhu G K. Fasthyperspectral anomaly detection via high-order 2-D crossing filter［J］. IEEE Transactions on Geoscience and Remote Sensing, 2014, 53(2): 620-630.

杨桄, 向英杰, 王琪, 田张男. 基于选择性分段行-列二维主成分分析的高光谱图像异常检测[J]. 激光与光电子学进展, 2018, 55(8): 081002. Yang Guang, Xiang Yingjie, Wang Qi, Tian Zhangnan. Anomaly Detection Based on Selective Segmentation Row-Column Two-Dimensional Principal Component Analysis for Hyperspectral Images[J]. Laser & Optoelectronics Progress, 2018, 55(8): 081002.

基于选择性分段行-列二维主成分分析的高光谱图像异常检测下载： 511次

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