高光谱图像全局异常检测RFS-SVDD算法

针对SVDD用于高光谱图像全局异常检测时存在虚警率高的问题,提出RFS-SVDD算法。RFS-SVDD将空间相邻且光谱相似的像元分为同一区域,根据区域大小将图像在空间上分成潜在异常区域与背景区域,用背景区域中所有子区域的平均光谱RFS作为SVDD训练样本求取支持向量。RFS是每个子区域中像元光谱的统计结果且不包含奇异像元,可以避免奇异像元光谱和图像随机噪声对背景建模的影响。对HYMAP和AVIRIS图像数据的仿真结果表明:RFS-SVDD算法能抑制异常目标像元光谱和图像随机噪声对背景建模的干扰,降低SVDD用于高光谱图像全局异常检测的虚警率。

A RFS-SVDD Algorithm for Hyperspectral Global Anomaly Detection

A RFS-SVDD algorithm was presented in order to decrease false-alarm rate when the SVDD detector is used for global anomaly detection in hyperspectral imagery. The original hyperspectral imagery was divided into some sub-regions by an improved spatial clustering algorithm proposed in this paper. Those sub-regions larger than the target size were defined as background sub-regions, and others were defined as potential anomaly sub-regions. The average spectrum of all the pixels in each background sub-region, which is called Region Feature Spectrum (RFS), was computed and used as training sample for SVDD. Those training samples were used to model the support region of the background spectrum. Because the RFS is the average spectrum of the background sub-region which contains no anomalous pixel, the modeling would not be affected by the anomalous spectrums and random noise in the hyperspectral imagery. The simulated results on the HYMAP and AVIRIS data show that the RFS-SVDD can eliminate the interference induced by the anomalous spectrum and/or random noise, and finally decreases the false-alarm rate when SVDD is used for global anomaly detection in hyperspectral imagery.