
Research on supervised LPP feature extraction for hyperspectral image
- Author
- Renbo Luo (UGent) , Youguo Pi and Wenzhi Liao (UGent)
- Organization
- Abstract
- For the classification among different land-cover types in a hyperspectral image, particularly in the small-sample-size problem, a feature extraction method is an approach for reducing the dimensionality and increasing the classification accuracy. A supervised principal locality preserving projection (SPLPP ) feature extraction algorithms, which uses the label information of training sample in locality preserving projection (LPP), was proposed in this paper. Three main steps are involved in the proposed SLPP: firstly uses PCA to remove redundant information, and then combines the label information in LPP, finally, SPLPP projects high-dimensional hyperspectral image into a low-dimensional space. Last but not least, SPLPP uses the extracted features as inputs of classifiers (e.g. support vector machine (SVM ) and K-nearest neighbors (KNN )) to do classification. Experimental results show that the proposed SPLPP has better local information retention ability and class discrimination ability compared with PCA, LPP, LDA
- Keywords
- Hyperspectral remote sensing image, Supervised principal locality preserving projection, Classification, Feature extraction
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-5840655
- MLA
- [1]R. Luo, Y. Pi, and W. Liao, “Research on supervised LPP feature extraction for hyperspectral image,” REMOTE SENSING TECHNOLOGY AND APPLICATION, vol. 27, no. 6, pp. 46–52, 2012.
- APA
- Luo, Renbo, et al. “Research on Supervised LPP Feature Extraction for Hyperspectral Image.” REMOTE SENSING TECHNOLOGY AND APPLICATION, edited by Ji Wu, vol. 27, no. 6, 2012, pp. 46–52.
- Chicago author-date
- Luo, R., Pi, Y., & Liao, W. (2012). Research on supervised LPP feature extraction for hyperspectral image. REMOTE SENSING TECHNOLOGY AND APPLICATION, 27(6), 46–52.
- Chicago author-date (all authors)
- Luo, Renbo, Youguo Pi, and Wenzhi Liao. 2012. “Research on Supervised LPP Feature Extraction for Hyperspectral Image.” Edited by Ji Wu. REMOTE SENSING TECHNOLOGY AND APPLICATION 27 (6): 46–52.
- Vancouver
- Luo, Renbo, Youguo Pi, and Wenzhi Liao. 2012. “Research on Supervised LPP Feature Extraction for Hyperspectral Image.” Ed by. Ji Wu. REMOTE SENSING TECHNOLOGY AND APPLICATION 27 (6): 46–52.
- IEEE
- 1.Luo R, Pi Y, Liao W. Research on supervised LPP feature extraction for hyperspectral image. Wu J, editor. REMOTE SENSING TECHNOLOGY AND APPLICATION. 2012;27(6):46–52.
@article{5840655, abstract = {{For the classification among different land-cover types in a hyperspectral image, particularly in the small-sample-size problem, a feature extraction method is an approach for reducing the dimensionality and increasing the classification accuracy. A supervised principal locality preserving projection (SPLPP ) feature extraction algorithms, which uses the label information of training sample in locality preserving projection (LPP), was proposed in this paper. Three main steps are involved in the proposed SLPP: firstly uses PCA to remove redundant information, and then combines the label information in LPP, finally, SPLPP projects high-dimensional hyperspectral image into a low-dimensional space. Last but not least, SPLPP uses the extracted features as inputs of classifiers (e.g. support vector machine (SVM ) and K-nearest neighbors (KNN )) to do classification. Experimental results show that the proposed SPLPP has better local information retention ability and class discrimination ability compared with PCA, LPP, LDA}}, author = {{Luo, Renbo and Pi, Youguo and Liao, Wenzhi}}, editor = {{Wu, Ji}}, issn = {{1004-0323}}, journal = {{REMOTE SENSING TECHNOLOGY AND APPLICATION}}, keywords = {{Hyperspectral remote sensing image,Supervised principal locality preserving projection,Classification,Feature extraction}}, language = {{chi}}, number = {{6}}, pages = {{46--52}}, title = {{Research on supervised LPP feature extraction for hyperspectral image}}, volume = {{27}}, year = {{2012}}, }