CAJ | 학술논문

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。
침대고광보도상혼합상원영향이상검측효과적문제，제출료일충기우단원제취적이상검측산법。해산법채용소파분해，장원시고광보도상분해위고빈신식도상화저빈신식도상，사기저빈신식도상，지이용고빈신식도상，종이억제료배경，돌출료목표；연후사용정교자공간투영（OSP）방법제취도상적단원광보；최후근거제취적단원광보，채용광보각필배（SAM）기술완성고광보도상적이상검측。위료험증본문방법적유효성，이용 AVIRIS 고광보수거진행료방진실험，취득료교호적검측효과。여기타산법상비，결과표명，본문산법적검측성능명현우우전통산법，기강저료허경솔，우대대축단료계산시간，괄용우실시적고광보도상이상목표검측。
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.