Information-Guided Diffusion Model for Downscaling Land Surface Temperature from SDGSAT-1 Remote Sensing Images DOI Creative Commons

Jianxin Wang,

Zhitao Fu, Bo‐Hui Tang

et al.

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(10), P. 1669 - 1669

Published: May 9, 2025

Land Surface Temperature (LST) is a parameter retrieved through the thermal infrared band of remote sensing satellites, and it crucial in various climate environmental models. Compared to other multispectral bands, bands have lower spatial resolution, which limits their practical applications. Taking Heihe River Basin China as case study, this research focuses on LST data from SDGSAT-1 using three-channel split-window algorithm. In paper, we propose novel approach, Information-Guided Diffusion Model (IGDM), apply downscale image. The results indicate that downscaling accuracy image proposed IGDM model outperforms Linear, Enhanced Deep Super-Resolution Network (EDSR), Convolutional Neural (SRCNN), Discrete Cosine Transform Local Spatial Attention (DCTLSA), Denoising Probabilistic Models (DDPM). Specifically, RMSE reduced by 55.16%, 51.29%, 48.39%, 52.88%, 17.18%. By incorporating auxiliary information, particularly when NDVI NDWI inputs, performance significantly improved. DDPM, decreased 0.666 0.574, MAE dropped 0.517 0.376, PSNR increased 38.55 40.27. Overall, highlight effectiveness information-guided diffusion generating high-resolution data. Additionally, study reveals feature impact different information variations features across regions temperature ranges.

Language: Английский

Information-Guided Diffusion Model for Downscaling Land Surface Temperature from SDGSAT-1 Remote Sensing Images DOI Creative Commons

Jianxin Wang,

Zhitao Fu, Bo‐Hui Tang

et al.

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(10), P. 1669 - 1669

Published: May 9, 2025

Land Surface Temperature (LST) is a parameter retrieved through the thermal infrared band of remote sensing satellites, and it crucial in various climate environmental models. Compared to other multispectral bands, bands have lower spatial resolution, which limits their practical applications. Taking Heihe River Basin China as case study, this research focuses on LST data from SDGSAT-1 using three-channel split-window algorithm. In paper, we propose novel approach, Information-Guided Diffusion Model (IGDM), apply downscale image. The results indicate that downscaling accuracy image proposed IGDM model outperforms Linear, Enhanced Deep Super-Resolution Network (EDSR), Convolutional Neural (SRCNN), Discrete Cosine Transform Local Spatial Attention (DCTLSA), Denoising Probabilistic Models (DDPM). Specifically, RMSE reduced by 55.16%, 51.29%, 48.39%, 52.88%, 17.18%. By incorporating auxiliary information, particularly when NDVI NDWI inputs, performance significantly improved. DDPM, decreased 0.666 0.574, MAE dropped 0.517 0.376, PSNR increased 38.55 40.27. Overall, highlight effectiveness information-guided diffusion generating high-resolution data. Additionally, study reveals feature impact different information variations features across regions temperature ranges.

Language: Английский

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