# Contrastive Learning for Regression on Hyperspectral Data

Source: [Contrastive Learning for Regression on Hyperspectral Data](<https://www.sota2.com/research/paper/68fa72b21e209131cea57fb5>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T06:32:05.519Z

Authors: Mohamad Dhaini, Maxime Berar, Paul Honeine, Antonin Van Exem

Published: 2024-02-12T21:33:46.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2403.17014>)

[PDF](<https://arxiv.org/pdf/2403.17014.pdf>)