基于光谱解译的高光谱图像奇异检测算法  

ANOMALY DETECTION ALGORITHM OF HYPERSPECTRAL IMAGES BASED ON SPECTRAL ANALYSES

作　　者：谷延锋[1] 刘颖[1] 贾友华[1] 张晔[1] 

机构地区：[1]哈尔滨工业大学信息工程系,黑龙江哈尔滨150001

出　　处：《红外与毫米波学报》2006年第6期473-477,共5页Journal of Infrared and Millimeter Waves

基　　金：国家自然科学基金(60472048和60402025)资助项目

摘　　要：奇异检测是高光谱图像的重要应用之一.针对目前研究中存在的问题,提出了一种新的基于光谱解译的奇异目标检测算法,用于高光谱图像处理.该算法利用光谱解译技术有效地实现了目标信息和复杂背景的分离,很好地抑制了背景对检测的干扰.解译后的误差数据仅包含丰富的目标信息且更好地服从高斯分布.利用主成分分析对解译误差数据进行变换,根据高阶统计量,定义局部平均奇异度来选择对于奇异检测最有效的主分量,并利用RX算子完成最终检测.为验证算法的有效性,利用真实的AVIR IS数据进行了仿真实验.结果表明该算法能够较大地改进经典RX算法的检测性能.Anomaly detection is an important application of hyperspectral images. Aiming at the problems in current researches, a new anomaly detection algorithm based on spectral analyses was proposed for hyperspectral images. The algorithm used spectral analyses technique to effectively separate target information from complicate backgrounds and greatly suppressed background interferences for detection. And those error data obtained after spectral unmixing with background endmembers only include abundant target information and better follow Gaussian distribution. Principal component analysis was used to transform the error data, and local average singularity was defined to select the most effective principal component for anomaly detection according to high-order statistics. Final detection was realized with conventional RX detector. To validate the effectiveness of the proposed algorithm, numerical experiments were conducted on real AVIRIS data. Experimental results show that the proposed algorithm greatly outperforms the conventional RX algorithm.

关 键 词：高光谱图像 奇异检测 主成分分析 

分 类 号：TP751.1[自动化与计算机技术—检测技术与自动化装置]