# Contrastive Learning for Regression on Hyperspectral Data

arXiv: https://arxiv.org/abs/2403.17014 · HTML: https://mlsanity.com/paper/2403.17014/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 17 references that are in the map.
- First posted on arXiv 12 Feb 2024.

## Builds on (most cited first, 4 of 4)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [HybridSN: Exploring 3D-2D CNN Feature Hierarchy for Hyperspectral Image Classification](https://mlsanity.com/paper/1902.06701/) (2019)
- [Nearest Neighbor-Based Contrastive Learning for Hyperspectral and LiDAR Data Classification](https://mlsanity.com/paper/2301.03335/) (2023)
- [Self Supervised Learning for Few Shot Hyperspectral Image Classification](https://mlsanity.com/paper/2206.12117/) (2022)

## Built on by (most cited first, 1 of 1)

- [Spectral-Spatial Contrastive Learning Framework for Regression on Hyperspectral Data](https://mlsanity.com/paper/2602.10745/) (2026)

## Abstract (arXiv)

Contrastive learning has demonstrated great effectiveness in representation learning especially for image classification tasks. However, there is still a shortage in the studies targeting regression tasks, and more specifically applications on hyperspectral data. In this paper, we propose a contrastive learning framework for the regression tasks for hyperspectral data. To this end, we provide a collection of transformations relevant for augmenting hyperspectral data, and investigate contrastive learning for regression. Experiments on synthetic and real hyperspectral datasets show that the proposed framework and transformations significantly improve the performance of regression models, achieving better scores than other state-of-the-art transformations.