CAJ | 학술논문

由于高光谱图像具有高阶性和背景分布特性复杂的特点，这使得现有的算法在解决异常检测问题时存在一些不足。通过分析高光谱图像的光谱特性和空间特性，基于统计学习理论，利用光谱解混技术和子空间划分方法，提出了基于光谱解混的选择性波段子集高光谱图像异常检测算法。该算法首先利用光谱解混技术提取出对背景分布特性有严重影响的端元光谱，由此降低背景干扰突出异常目标信息；在此基础上，利用子空间划分方法将整个波段空间划分为大小不等的多个子空间，并在每个子空间内利用非高斯程度度量准则提取出富含异常目标信息的特征波段；最后，采用 KRX 算法作为异常检测算子完成异常目标检测。利用真实的高光谱图像对提出的算法进行实验验证，结果表明该算法是有效和合理的，具有良好的异常检测性能。
유우고광보도상구유고계성화배경분포특성복잡적특점，저사득현유적산법재해결이상검측문제시존재일사불족。통과분석고광보도상적광보특성화공간특성，기우통계학습이론，이용광보해혼기술화자공간화분방법，제출료기우광보해혼적선택성파단자집고광보도상이상검측산법。해산법수선이용광보해혼기술제취출대배경분포특성유엄중영향적단원광보，유차강저배경간우돌출이상목표신식；재차기출상，이용자공간화분방법장정개파단공간화분위대소불등적다개자공간，병재매개자공간내이용비고사정도도량준칙제취출부함이상목표신식적특정파단；최후，채용 KRX 산법작위이상검측산자완성이상목표검측。이용진실적고광보도상대제출적산법진행실험험증，결과표명해산법시유효화합리적，구유량호적이상검측성능。
The current anomaly detection algorithms are shortage to solving anomaly detection problem because hyperspectral imagery has a higher order features and complicated background information distribution characteristics. By analyzing the spectral features and the spatial features, and exploiting spectral unmixing and subspace divided methods, based on statistical learning theory, an algorithm of anomaly detection of hyperspectral imagery selectivity band subsets is proposed based on spectral unmixing(UNBS-KRX). At first, hyperspectral imagery reduce the background interference and prominent anomaly target information by using spectral unmixing methods, which extract endmembers spectral, that is great influence on background information distribution of hyperspectral imagery. Then, the algorithm divides the whole bands space to a few subspaces. The size of the subspace is different, and non-Gaussian measurement criterion is used to extract the characteristic bands in each subspace. The bands are rich in anomaly target information. At last, as an anomaly detection operator, the kernel RX completes anomaly target detection. The real hyperspectral data sets are used in the experiments, and the result shows the UNBS-KRX is effective and reasonable, and has an execllent detection performance.