CAJ | 학술논문

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.
침대SVDD용우고광보도상전국이상검측시존재허경솔고적문제,제출RFS-SVDD산법.RFS-SVDD장공간상린차광보상사적상원분위동일구역,근거구역대소장도상재공간상분성잠재이상구역여배경구역,용배경구역중소유자구역적평균광보RFS작위SVDD훈련양본구취지지향량.RFS시매개자구역중상원광보적통계결과차불포함기이상원,가이피면기이상원광보화도상수궤조성대배경건모적영향.대HYMAP화AVIRIS도상수거적방진결과표명:RFS-SVDD산법능억제이상목표상원광보화도상수궤조성대배경건모적간우,강저SVDD용우고광보도상전국이상검측적허경솔.
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.