Self-supervised Learning for Hyperspectral Images of Trees

• URL: http://arxiv.org/abs/2509.05630v1
• Date: Sat, 06 Sep 2025 07:25:39 GMT
• Title: Self-supervised Learning for Hyperspectral Images of Trees
• Authors: Moqsadur Rahman, Saurav Kumar, Santosh S. Palmate, M. Shahriar Hossain,
• Abstract summary: This paper focuses on self-supervised learning to create neural network embeddings reflecting vegetation properties of trees from aerial hyperspectral images of crop fields.<n> 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.
• Score: 2.2399170518036913
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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