SEMI-SUPERVISED MARGINAL FISHER ANALYSIS FOR HYPERSPECTRAL IMAGE CLASSIFICATION
The problem of learning with both labeled and unlabeled examples arises frequently in Hyperspectral image (HSI) classification. While marginal Fisher analysis is a supervised method, which cannot be directly applied for Semi-supervised classification. In this paper, we proposed a novel method, cal...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2012-07-01
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| Series: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/I-3/377/2012/isprsannals-I-3-377-2012.pdf |
| Summary: | The problem of learning with both labeled and unlabeled examples arises frequently in Hyperspectral image (HSI) classification. While
marginal Fisher analysis is a supervised method, which cannot be directly applied for Semi-supervised classification. In this paper, we
proposed a novel method, called semi-supervised marginal Fisher analysis (SSMFA), to process HSI of natural scenes, which uses a
combination of semi-supervised learning and manifold learning. In SSMFA, a new difference-based optimization objective function
with unlabeled samples has been designed. SSMFA preserves the manifold structure of labeled and unlabeled samples in addition
to separating labeled samples in different classes from each other. The semi-supervised method has an analytic form of the globally
optimal solution, and it can be computed based on eigen decomposition. Classification experiments with a challenging HSI task
demonstrate that this method outperforms current state-of-the-art HSI-classification methods. |
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| ISSN: | 2194-9042 2194-9050 |