RECREATE: Supervised contrastive learning and inpainting based hyperspectral image denoising

Abstract

Hyperspectral image (HSI) contains information at various spectra, making it valuable in several real-world applications such as environmental monitoring, agriculture, and remote sensing. However, the acquisition process often introduces noise, necessitating effective HSI denoising methods to maintain its applicability. Deep Learning (DL) is considered as the de-facto for HSI denoising, but it requires a significant number of training samples to optimize network parameters for effective denoising outcomes. However, obtaining extensive datasets is challenging in HSI, leading to epistemic uncertainties and thereby deteriorating the denoising performance. This paper introduces a novel supervised contrastive learning (SCL) method, RECREATE, to enhance feature learning and mitigate the issue of epistemic uncertainty for HSI denoising. Furthermore, we introduce the exploration of image inpainting as an auxiliary task to enhance the HSI denoising performance. By adding HSI inpainting to CL, our method essentially enhances HSI denoising by increasing training datasets and enforcing improved feature learning. Experimental outcomes on various HSI datasets validate the efficacy of RECREATE, showcasing its potential for integration with existing HSI denoising techniques to enhance their performance, both qualitatively and quantitatively. This innovative method holds promise for addressing the limitations posed by limited training data and thereby advancing the field toward proposing better HSI denoising methods. © 2026 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS)