Spectral-Spatial Contrastive Learning Framework for Regression on Hyperspectral Data
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Abstract (arXiv)
Contrastive learning has demonstrated great success in representation learning, especially for image classification tasks. However, there is still a shortage in studies targeting regression tasks, and more specifically applications on hyperspectral data. In this paper, we propose a spectral-spatial contrastive learning framework for regression tasks for hyperspectral data, in a model-agnostic design allowing to enhance backbones such as 3D convolutional and transformer-based networks. Moreover, we provide a collection of transformations relevant for augmenting hyperspectral data. Experiments on synthetic and real datasets show that the proposed framework and transformations significantly improve the performance of all studied backbone models.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 8 of its 21 references that are in the map. First posted on arXiv 11 Feb 2026.
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References · 8
- 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
- 2019HybridSN: Exploring 3D-2D CNN Feature Hierarchy for Hyperspectral Image Classificationcited by 91
- 2018Three dimensional Deep Learning approach for remote sensing image classificationcited by 50
- 2024SpectralEarth: Training Hyperspectral Foundation Models at Scalecited by 42
- 2018HSI-CNN: A Novel Convolution Neural Network for Hyperspectral Imagecited by 14
- 2023Nearest Neighbor-Based Contrastive Learning for Hyperspectral and LiDAR Data Classificationcited by 12
- 20243D-Convolution Guided Spectral-Spatial Transformer for Hyperspectral Image Classificationcited by 4
- 2024Contrastive Learning for Regression on Hyperspectral Datacited by 1
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