Adaptation of Diffusion Models for Remote Sensing Imagery

Egileak: Adriano Ettari Antonio Nappa Marco Quartulli Izar Azpiroz Iragorri Giuseppe Longo

Data: 07.07.2024


Abstract

The present contribution focuses on applying Denoising Diffusion Probabilistic Models to Remote Sensing image classification, generation and super-resolution. Diffusion models are enhanced, including an attention block embedded in a UNet architecture is used to generate images to complement the EuroSAT data set. Furthermore, models with the same architecture super-resolve sentinel-2 optical images . The results indicate that diffusion models with attention can provide a promising methodological path for applications such as estimating the NDVI images most probably associated with given electro-optical and SAR acquisitions thereby overcoming limitations in observability due to cloud cover or solar illumination.

BIB_text

@Article {
title = {Adaptation of Diffusion Models for Remote Sensing Imagery},
pages = {7240-7243},
keywds = {
Diffusion Models; NDVI; SAR; Satellite images
}
abstract = {

The present contribution focuses on applying Denoising Diffusion Probabilistic Models to Remote Sensing image classification, generation and super-resolution. Diffusion models are enhanced, including an attention block embedded in a UNet architecture is used to generate images to complement the EuroSAT data set. Furthermore, models with the same architecture super-resolve sentinel-2 optical images . The results indicate that diffusion models with attention can provide a promising methodological path for applications such as estimating the NDVI images most probably associated with given electro-optical and SAR acquisitions thereby overcoming limitations in observability due to cloud cover or solar illumination.


}
isbn = {979-8-3503-6032-5},
date = {2024-07-07},
}
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