CAJ | 학술논문

针对高光谱数据维数高、数据量大、信息冗余多、波段相关性强等特点，在综合各种数据降维方法的基础上，提出一种基于最佳波段组合的高光谱遥感影像分类方法。以美国印第安纳州地区的AVIRIS数据为例，分析各波段信息量和相邻波段的相关性，利用子空间划分、分段波段指数选择法，进行特征波段的选择；并针对难区分地物类别，应用J-M距离模型对其可分性进行判别，获得最佳波段组合。最后采用支持向量机分类器进行分类。实验结果表明，采用最佳波段组合方法，可以有效地提高高光谱的分类精度。
침대고광보수거유수고、수거량대、신식용여다、파단상관성강등특점，재종합각충수거강유방법적기출상，제출일충기우최가파단조합적고광보요감영상분류방법。이미국인제안납주지구적AVIRIS수거위례，분석각파단신식량화상린파단적상관성，이용자공간화분、분단파단지수선택법，진행특정파단적선택；병침대난구분지물유별，응용J-M거리모형대기가분성진행판별，획득최가파단조합。최후채용지지향량궤분류기진행분류。실험결과표명，채용최가파단조합방법，가이유효지제고고광보적분류정도。
Comparing with traditional remote sensing data, high dimension , the large amount of information , data redundancy and strong band relevant are prevalent in the hyperspetral remote sensing data .On the combination of data dimension reduction methods , this paper puts forward a hyperspectral remote sensing image classification method based on optimum band combination .Taking AVIRIS data in Indiana area as an example , we analyze the spectrum information and the adjacent band correlation .Then we use sub-space division and band index , select the feature band .For the classes difficult to distinguish , we apply J-M distance model for its separability criterion to point out the optimal band combination .Finally, we use SVM classifier for classification .The experimental re-sults show that using the optimal band combination can effectively improve the classification accuracy of hyperspectral remote sensing data.