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.

本论文研究了如何使用深度学习算法，特别是多重残差U-Net模型，对MODIS LST遥感图像进行超分辨率处理，从而实现将遥感图像从1Km提高到250m的像素分辨率。实验结果表明，多重残差U-Net模型表现优于其他目前主流的方法。

MODIS地表温度超分辨率的卷积神经网络建模