摘要
针对现有的高光谱图像异常检测算法大多只注重挖掘目标与背景光谱上的差异,而忽略二者在空间结构上的差异,导致检测结果不佳的问题,提出了一种基于空谱联合的异常检测算法。为保留图像的空间结构信息,所提算法逐波段进行异常检测,通过建立双窗计算待测像素与背景亮度上的差异来衡量待测像素的光谱异常程度;然后将内窗作为待测像素的空间结构窗,寻找背景中与其最相似的空间结构窗,通过计算二者的差异来衡量待测像素的空间结构异常程度,综合光谱异常程度和空间异常程度即可得到待测像素相对背景的异常指数。遍历整个图像,将各个波段像素的异常指数对应相加即为算法的检测结果。在3组高光谱数据上的实验结果表明:与现有的异常检测算法相比,所提算法能够显著降低探测的虚警率,并且对噪声具有很好的稳定性。
Most of the existing anomaly detection algorithms for hyperspectral image only focus on the spectrum differences between the target and background while ignoring the spatial structure differences,which leads to poor detection results.Aiming at this issue,we propose a novel algorithm based on the combination of spectral and spatial information for anomaly detection (SSAD).To preserve the spatial structure information of the image,we detect anomalies band by band.The dual windows are established to calculate the luminance differences between the pixel under test (PUT)and background,and the spectral anomaly degree of PUT is measured.Then the inner window is regarded as the spatial structure window of PUT,and the most similar spatial structure window with the spatial structure window of PUT is searched from the background.The differences between the two is calculated to measure the spatial structure anomaly degree of PUT.Thus,the anomaly index of the PUT is obtained by the measurement of spectral and spatial anomaly degree.Going through the whole image,the detection result of the algorithm is acquired by summing up the anomaly index of each band correspondingly.Experimental results on three hyperspectral data show that,compared with existing anomaly detection algorithms,the proposed algorithm can significantly reduce the false alarm rate and has good robust to noise.
作者
鞠荟荟
刘志刚
汪洋
Ju Huihui;Liu Zhigang;Wang Yang(Institute of Nuclear Engineering,Rocket Force Engineering University,Xi'an,Shaanxi 710025,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2018年第12期485-492,共8页
Laser & Optoelectronics Progress
基金
国家自然科学基金(41574008)
关键词
遥感
高光谱图像
异常检测
光谱异常
空间结构异常
remote sensing
hyperspectral image
anomaly detection
spectral anomaly
spatial structure anomaly
