# Spectral-Spatial Contrastive Learning Framework for Regression on Hyperspectral Data

Source: [Spectral-Spatial Contrastive Learning Framework for Regression on Hyperspectral Data](<https://www.sota2.com/research/paper/698d5ec7c97a0b3e3b5e3f28>)

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

Data updated: 2026-02-13T05:00:04.184Z

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

Published: 2026-02-11T11:16:57.000Z

## Abstract

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.

## 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/2602.10745>)

[PDF](<https://arxiv.org/pdf/2602.10745.pdf>)