Hyperspectral remote sensing image classification based on SSMFA and kNNS

被引：0

作者：

Wang, Li-Zhi ^{[1
]}

Huang, Hong ^{[1
]}

Feng, Hai-Liang ^{[1
]}

机构：

[1] Key Laboratory on Opto-Electronic Technique and Systems, Ministry of Education, Chongqing University

来源：

Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2012年 / 40卷 / 04期

关键词：

Graph embedding framework; Hyperspectral images; Land cover classification; Nearest neighbor;

D O I：

10.3969/j.issn.0372-2112.2012.04.026

中图分类号：

学科分类号：

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.

引用

页码：780 / 787

页数：7

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