Self-supervised Learning for Hyperspectral Images of Trees
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866914025962471424 |
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| author | Rahman, Moqsadur Kumar, Saurav Palmate, Santosh S. Hossain, M. Shahriar |
| author_facet | Rahman, Moqsadur Kumar, Saurav Palmate, Santosh S. Hossain, M. Shahriar |
| contents | Aerial remote sensing using multispectral and RGB imagers has provided a critical impetus to precision agriculture. Analysis of the hyperspectral images with limited or no labels is challenging. This paper focuses on self-supervised learning to create neural network embeddings reflecting vegetation properties of trees from aerial hyperspectral images of crop fields. Experimental results demonstrate that a constructed tree representation, using a vegetation property-related embedding space, performs better in downstream machine learning tasks compared to the direct use of hyperspectral vegetation properties as tree representations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_05630 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Self-supervised Learning for Hyperspectral Images of Trees Rahman, Moqsadur Kumar, Saurav Palmate, Santosh S. Hossain, M. Shahriar Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Aerial remote sensing using multispectral and RGB imagers has provided a critical impetus to precision agriculture. Analysis of the hyperspectral images with limited or no labels is challenging. This paper focuses on self-supervised learning to create neural network embeddings reflecting vegetation properties of trees from aerial hyperspectral images of crop fields. Experimental results demonstrate that a constructed tree representation, using a vegetation property-related embedding space, performs better in downstream machine learning tasks compared to the direct use of hyperspectral vegetation properties as tree representations. |
| title | Self-supervised Learning for Hyperspectral Images of Trees |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2509.05630 |