关键词: 高光谱影像, 地物分类, 图嵌入框架, 最近邻

Abstract: In order to explore dimensionality reduction and classification in hyperspectral remote sensing image,an algorithm based on semi-supervised marginal Fisher analysis(SSMFA) and k-nearest-neighbor simplex(kNNS) is proposed in this paper.First,the data are projected from a high-dimensional space onto low-dimensional space by SSMFA combined with the information of different classes.Then,classification is performed under the kNNS classifier by using a few neighbors from each class.The experimental results on the Urban data set,Washington DC Mall data set and Indian Pine data set show the effectiveness of the proposed algorithm,when i(i=4,6,8) labeled samples and 10 unlabeled samples of each class are randomly selected for training and 100 samples of each class for testing,the overall accuracy of our proposed algorithm is improved by 0.8%-2.5%,2.8%-4.5% and 4.0%-7.0%,respectively,as compared with MFA+kNNS,MFA+kNN and other methods.

Key words: hyperspectral images, land cover classification, graph embedding framework, nearest neighbor