Local Patch Discriminative Metric Learning for Hyperspectral Image Feature Extraction
| Autor: | Yuan Tian, Lefei Zhang, Qian Zhang, Lubin Weng, Yiping Yang |
|---|---|
| Rok vydání: | 2014 |
| Předmět: |
Contextual image classification
business.industry Feature extraction Hyperspectral imaging Pattern recognition Geotechnical Engineering and Engineering Geology Discriminative model Feature (computer vision) Metric (mathematics) Principal component analysis Artificial intelligence Electrical and Electronic Engineering Projection (set theory) business Mathematics |
| Zdroj: | IEEE Geoscience and Remote Sensing Letters. 11:612-616 |
| ISSN: | 1558-0571 1545-598X |
| DOI: | 10.1109/lgrs.2013.2272476 |
| Popis: | In hyperspectral image (HSI) classification, feature extraction is one important step. Traditional methods, e.g., principal component analysis (PCA) and locality preserving projection, usually neglect the information of within-class similarity and between-class dissimilarity, which is helpful to the improvement of classification. On the other hand, most of these methods, e.g., PCA and linear discriminative analysis, consider that the HSI data lie on a low-dimensional manifold or each class is on a submanifold. However, some class data of HSI may lie on a multimanifold. To avoid these problems, we propose a method for feature extraction in HSIs, assuming that a local region resides on a submainfold. In our method, we deal with the data region by region by taking into account the different discriminative locality information. Then, under the metric learning framework, a robust distance metric is learned. It aims to learn a subspace in which the samples in the same class are as near as possible while the samples in different classes are as far as possible. Encouraging experimental results on two available hyperspectral data sets indicate that our proposed algorithm outperforms many existing feature extract methods for HSI classification. |
| Databáze: | OpenAIRE |
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