Adaptation of Diffusion Models for Remote Sensing Imagery
Autores: Adriano Ettari Giuseppe Longo
Fecha: 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
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},
}