{"ID":2873924,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.05630","arxiv_id":"2509.05630","title":"Self-supervised Learning for Hyperspectral Images of Trees","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.","short_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...","url_abs":"https://arxiv.org/abs/2509.05630","url_pdf":"https://arxiv.org/pdf/2509.05630v1","authors":"[\"Moqsadur Rahman\",\"Saurav Kumar\",\"Santosh S. Palmate\",\"M. Shahriar Hossain\"]","published":"2025-09-06T07:25:39Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\",\"cs.LG\"]","methods":"[]","has_code":false}