Feature Extraction of Hyperspectral Image Using Semi-supervised Sparse Manifold Embedding

LUO Fulin; HUANG Hong; LIU Jiamin; FENG Hailiang

doi:10.11999/JEIT151340

Volume 38 Issue 9

Sep. 2016

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Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(9): 2321-2329

LUO Fulin, HUANG Hong, LIU Jiamin, FENG Hailiang. Feature Extraction of Hyperspectral Image Using Semi-supervised Sparse Manifold Embedding[J]. Journal of Electronics & Information Technology, 2016, 38(9): 2321-2329. doi: 10.11999/JEIT151340

Citation:

LUO Fulin, HUANG Hong, LIU Jiamin, FENG Hailiang. Feature Extraction of Hyperspectral Image Using Semi-supervised Sparse Manifold Embedding[J]. Journal of Electronics & Information Technology, 2016, 38(9): 2321-2329. doi: 10.11999/JEIT151340

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Feature Extraction of Hyperspectral Image Using Semi-supervised Sparse Manifold Embedding

doi: 10.11999/JEIT151340

Funds:

The Chongqing Postgraduates Innovation Project (CYB15052), The National Natural Science Foundation of China (41371338), The Basic and Advanced Research Program of Chongqing (cstc2013jcyjA40005), The Fundamental Research Funds for the Central Universities (106112013CDJZR125501, 1061120131204)

Received Date: 2015-11-23
Rev Recd Date: 2016-03-18
Publish Date: 2016-09-19

Abstract

Abstract

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.
- Hyperspectral data,
- Feature extraction,
- Semi-supervised learning,
- Sparse manifold embedding

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References(23)

References

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