Cross-View-Prediction: Exploring Contrastive Feature for Hyperspectral Image Classification
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2022
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| _version_ | 1866915048151056384 |
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| author | Zhang, Anyu Wu, Haotian Cao, Zeyu |
| author_facet | Zhang, Anyu Wu, Haotian Cao, Zeyu |
| contents | This paper presents a self-supervised feature learning method for hyperspectral image classification. Our method tries to construct two different views of the raw hyperspectral image through a cross-representation learning method. And then to learn semantically consistent representation over the created views by contrastive learning method. Specifically, four cross-channel-prediction based augmentation methods are naturally designed to utilize the high dimension characteristic of hyperspectral data for the view construction. And the better representative features are learned by maximizing mutual information and minimizing conditional entropy across different views from our contrastive network. This 'Cross-View-Predicton' style is straightforward and gets the state-of-the-art performance of unsupervised classification with a simple SVM classifier. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2203_07000 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Cross-View-Prediction: Exploring Contrastive Feature for Hyperspectral Image Classification Zhang, Anyu Wu, Haotian Cao, Zeyu Computer Vision and Pattern Recognition I.4.9 This paper presents a self-supervised feature learning method for hyperspectral image classification. Our method tries to construct two different views of the raw hyperspectral image through a cross-representation learning method. And then to learn semantically consistent representation over the created views by contrastive learning method. Specifically, four cross-channel-prediction based augmentation methods are naturally designed to utilize the high dimension characteristic of hyperspectral data for the view construction. And the better representative features are learned by maximizing mutual information and minimizing conditional entropy across different views from our contrastive network. This 'Cross-View-Predicton' style is straightforward and gets the state-of-the-art performance of unsupervised classification with a simple SVM classifier. |
| title | Cross-View-Prediction: Exploring Contrastive Feature for Hyperspectral Image Classification |
| topic | Computer Vision and Pattern Recognition I.4.9 |
| url | https://arxiv.org/abs/2203.07000 |