CAJ | 학술논문

提出了一种融合光谱和空间结构信息的高光谱遥感影像增量分类算法 INC_SPEC_MPext.通过主成分分析(PCA)提取高光谱影像的若干主成分,利用数学形态学提取各主分量影像对应的形态学剖面(MP),再将所有主分量影像的形态学剖面归并联结,组成扩展的形态学剖面(MPext ).将MPext与光谱信息相结合以增加知识,最大限度地挖掘未标记样本的有用信息,优化分类器的学习能力.不断从分类器对未标记样本的预测结果中甄选置信度高的样本加入训练集,并迭代地利用扩大的训练集进行分类器构建和样本预测.以不同地表覆盖类型的AVIRIS Indian Pines和Hyperion EO-1 Botswana 作为测试数据,分别与基于光谱、MPext、光谱和MPext融合的分类方法进行比对.试验结果表明,在训练样本数量有限情况下,INC_SPEC_MPext算法在降低分类成本的同时,分类精度和 Kappa 系数都有不同程度的提高.
제출료일충융합광보화공간결구신식적고광보요감영상증량분류산법 INC_SPEC_MPext.통과주성분분석(PCA)제취고광보영상적약간주성분,이용수학형태학제취각주분량영상대응적형태학부면(MP),재장소유주분량영상적형태학부면귀병련결,조성확전적형태학부면(MPext ).장MPext여광보신식상결합이증가지식,최대한도지알굴미표기양본적유용신식,우화분류기적학습능력.불단종분류기대미표기양본적예측결과중견선치신도고적양본가입훈련집,병질대지이용확대적훈련집진행분류기구건화양본예측.이불동지표복개류형적AVIRIS Indian Pines화Hyperion EO-1 Botswana 작위측시수거,분별여기우광보、MPext、광보화MPext융합적분류방법진행비대.시험결과표명,재훈련양본수량유한정황하,INC_SPEC_MPext산법재강저분류성본적동시,분류정도화 Kappa 계수도유불동정도적제고.
An incremental classification algorithm INC_SPEC_MPext was proposed for hyperspectral remote sensing images based on spectral and spatial information.The spatial information was extracted by building morphological profiles based on several principle components of hyperspectral image.The morpho-logical profiles were combined together in extended morphological profiles (MPext).Combine spectral and MPext to enrich knowledge and utilize the useful information of unlabeled data at the most extent to optimize the classifier.Pick out high confidence data and add to training set,then retrain the classifier with augmented training set to predict the rest samples.The process was performed iteratively.The proposed algorithm was tested on AVIRIS Indian Pines and Hyperion EO-1 Botswana data,which take on different covers,and experimental results show low classification cost and significant improvements in terms of accuracies and Kappa coefficient under limited training samples compared with the classification results based on spectral,MPext and the combination of sepctral and MPext.