Feature extraction of hyperspectral image using semi-supervised sparse manifold embedding

被引：0
|
作者：
Luo F. [1 ]
Huang H. [1 ]
Liu J. [1 ]
Feng H. [1 ]
机构：
[1] Key Laboratory of Optoelectronic Technique System of the Ministry of Education, Chongqing University, Chongqing
来源：
Huang, Hong (hhuang@cqu.edu.cn) | 1600年 / Science Press卷 / 38期
基金：
中国国家自然科学基金;
关键词：
Feature extraction; Hyperspectral data; Semi-supervised learning; Sparse manifold embedding;
D O I：
10.11999/JEIT151340
中图分类号：
学科分类号：
摘要：
Hyperspectral image contains the properties of much bands and high redundancy, and the research of hyperspectral image classification focuses on feature extraction. To overcome this problem, a Semi-Supervised Sparse Manifold Embedding (S3ME) algorithm is proposed in this paper. The S3ME method makes full use of labeled and unlabeled samples to adaptively reveal the similarity relationship between data with the sparse representation of tangent space. It constructs a semi-supervised similarity graph via the sparse coefficients and enhances the weight between labeled samples from the same class. In a low-dimensional embedding space, S3ME preserves the similarity of graph to minimize the sum of the weighted distance. Then, it obtains a projection matrix for feature extraction. S3ME not only reveals the sparse manifold structure of data but also enhances the compactness of the same class data, which can effectively extract the discriminating feature and improve the classification performance. The overall classification accuracies of the proposed S3ME method respectively reach 84.62% and 88.07% on the PaviaU and Salinas hyperspectral data sets, and the classification performance of land cover is improved compared with the traditional feature extraction methods. © 2016, Science Press. All right reserved.
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页码：2321 / 2329
页数：8
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