中文摘要：

为了研究高光谱影像数据的维数约简和分类问题,提出了一种基于半监督边际费希尔分析（SSMFA）和kNNS的高光谱遥感影像数据分类算法.该方法利用有标记数据和无标记数据的信息获得数据的内在流形结构,通过SSMFA将高光谱数据从高维观测空间投影到低维流形空间,然后利用邻域内多个近邻点的信息通过kNNS分类器对低维空间中的数据进行分类.在Urban、Washington和Indian Pine数据集上的分类识别实验表明,该方法能够较为有效地发现高维空间中数据的内蕴结构,在每类随机选取4,6,8个有类别标记的样本10个无类别标记的样本的情况下,该方法的总体分类精度能够比MFA＋kNNS提高0.8%~2.5%,比MFA＋kNN提高2.8%~4.5%,比其他算法提高4.0%~7.0%,分类精度有了明显的提高.

英文摘要：

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.