Semi-supervised feature learning for hyperspectral image classification

Pengfei Zhang; Liujuan Cao; Cheng Wang; Jonathan Li

doi:10.1117/12.2234855

2 March 2016 Semi-supervised feature learning for hyperspectral image classification

Pengfei Zhang, Liujuan Cao, Cheng Wang, Jonathan Li

Proceedings Volume 9901, 2nd ISPRS International Conference on Computer Vision in Remote Sensing (CVRS 2015); 99010F (2016) https://doi.org/10.1117/12.2234855
Event: 2015 ISPRS International Conference on Computer Vision in Remote Sensing, 2015, Xiamen, China

Abstract

Hyperspectral image has high-dimensional Spectral–spatial features, those features with some noisy and redundant information. Since redundant features can have significant adverse effect on learning performance. So efficient and robust feature selection methods are make the best of labeled and unlabeled points to extract meaningful features and eliminate noisy ones. On the other hand, obtaining sufficient accurate labeled data is either impossible or expensive. In order to take advantage of both precious labeled and unlabeled data points, in this paper, we propose a new semisupervised feature selection method, Firstly, we use labeled points are to enlarge the margin between data points from different classes; Secondly, we use unlabeled points to find the local structure of the data space; Finally, we compare our proposed algorithm with Fisher score, PCA and Laplacian score on HSI classification. Experimental results on benchmark hyperspectral data sets demonstrate the efficiency and effectiveness of our proposed algorithm.

Citation Download Citation

Pengfei Zhang, Liujuan Cao, Cheng Wang, and Jonathan Li "Semi-supervised feature learning for hyperspectral image classification", Proc. SPIE 9901, 2nd ISPRS International Conference on Computer Vision in Remote Sensing (CVRS 2015), 99010F (2 March 2016); https://doi.org/10.1117/12.2234855

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KEYWORDS

Hyperspectral imaging

Feature selection

Principal component analysis

Image classification

Dimension reduction

Feature extraction

Data modeling

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