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Convolutional Neural Network Modelling for MODIS Land Surface Temperature Super-Resolution

Abstract : Nowadays, thermal infrared satellite remote sensors enable to extract very interesting information at large scale, in particular Land Surface Temperature (LST). However such data are limited in spatial and/or temporal resolutions which prevents from an analysis at fine scales. For example, MODIS satellite provides daily acquisitions with 1Km spatial resolutions which is not sufficient to deal with highly heterogeneous environments as agricultural parcels. Therefore, image super-resolution is a crucial task to better exploit MODIS LSTs. This issue is tackled in this paper. We introduce a deep learning-based algorithm, named Multi-residual U-Net, for super-resolution of MODIS LST single-images. Our proposed network is a modified version of U-Net architecture, which aims at super-resolving the input LST image from 1Km to 250m per pixel. The results show that our Multi-residual U-Net outperforms other state-of-the-art methods.
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https://hal.archives-ouvertes.fr/hal-03580148
Contributor : Carlos Granero Belinchon Connect in order to contact the contributor
Submitted on : Thursday, March 31, 2022 - 3:59:38 PM
Last modification on : Friday, August 5, 2022 - 2:54:52 PM

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EUSIPCO2022_TIR.pdf
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  • HAL Id : hal-03580148, version 2
  • ARXIV : 2202.10753

Citation

Binh Minh Nguyen, Ganglin Tian, Minh-Triet Vo, Aurélie Michel, Thomas Corpetti, et al.. Convolutional Neural Network Modelling for MODIS Land Surface Temperature Super-Resolution. 2022. ⟨hal-03580148v2⟩

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