红外技术, 2015, 37 (10): 836, 网络出版: 2015-12-21
基于端元提取的高光谱异常目标检测
Anomaly Detection Algorithm Based on Endmember Extraction in Hyperspectral Imagery
高光谱图像 异常检测 端元提取 小波分解 正交子空间投影 hyperspectral imagery anomaly detection endmember extraction wavelet decomposition orthogonal subspace projection
摘要
针对高光谱图像混合像元影响异常检测效果的问题, 提出了一种基于端元提取的异常检测算法。该算法采用小波分解, 将原始高光谱图像分解为高频信息图像和低频信息图像, 舍弃低频信息图像, 只利用高频信息图像, 从而抑制了背景, 突出了目标;然后使用正交子空间投影( OSP)方法提取图像的端元光谱;最后根据提取的端元光谱, 采用光谱角匹配( SAM)技术完成高光谱图像的异常检测。为了验证本文方法的有效性, 利用 AVIRIS高光谱数据进行了仿真实验, 取得了较好的检测效果。与其他算法相比, 结果表明, 本文算法的检测性能明显优于传统算法, 既降低了虚警率, 又大大缩短了计算时间, 适用于实时的高光谱图像异常目标检测。
Abstract
In order to overcome the bad influence caused by mixed pixels in hyperspectral anomaly detection, a new target detection algorithm based on endmember extraction method is proposed. Hyperspectral imagery is decomposed into high frequency and low frequency images by wavelet decomposition firstly. High frequency images used only and sight of low frequency images abandoned, background information is effectively suppressed and anomaly targets become obvious consequently. And then the endmember spectral profile is got from high frequency images by Orthogonal Subspace Projection (OSP) algorithm. At last, anomaly detection is done by Spectral Angle Mapping (SAM) in the light of extracted endmember spectra. The proposed algorithm is studied using real hyperspectral data, and good detection effect is obtained. The results show that the proposed method which needs less time and has lower false alarm rate is proved to be better than the traditional algorithm, thus it is suitable for real-time anomaly detection in hyperspectral imagery.
何高攀, 杨桄, 张筱晗, 黄俊华, 孟强强. 基于端元提取的高光谱异常目标检测[J]. 红外技术, 2015, 37(10): 836. HE Gao-pan, YANG Guang, ZHANG Xiao-han, HUANG Jun-hua, MENG Qiang-qiang. Anomaly Detection Algorithm Based on Endmember Extraction in Hyperspectral Imagery[J]. Infrared Technology, 2015, 37(10): 836.