论文标题
MODIS土地表面温度超分辨率的卷积神经网络建模
Convolutional Neural Network Modelling for MODIS Land Surface Temperature Super-Resolution
论文作者
论文摘要
如今，热红外卫星遥控传感器可以大规模提取非常有趣的信息，特别是陆地表面温度（LST）。但是，此类数据在空间和/或时间分辨率方面受到限制，这些分辨率可以防止在细大范围内进行分析。例如，MODIS卫星每天提供1公里的空间决议，这不足以应对高度异质环境作为农业包裹。因此，图像超分辨率是更好地利用MODIS LST的关键任务。本文解决了这个问题。我们介绍了一种基于深度学习的算法，称为Modis LST单像的超级分辨率。我们提出的网络是U-NET体系结构的修改版本，该版本旨在将输入LST图像从1km到250m，每个像素从1公里到250m。结果表明，我们的多分离U-NET的表现优于其他最先进的方法。
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.
