基于MODIS数据的SMOS全球海面盐度产品的深度卷积网络降尺度研究

Proceedings of the 2019 3rd International Conference on Advances in Image Processing Pub Date : 2019-11-08 DOI:10.1145/3373419.3373462

Qixin Liu, Linlin Xu, Zhiwen Zhang

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引用次数: 2

摘要

降尺度是将粗域卫星产品转换为精细空间分辨率的重要过程。为了提高SMOS卫星全球海表盐度(SSS)数据的空间分辨率，设计了一种基于深度学习的降尺度方法。该算法能够高效有效地利用高空间分辨率中分辨率成像光谱仪(MODIS)卫星数据，提高SMOS SSS产品的空间分辨率。

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The Downscaling of the SMOS Global Sea Surface Salinity Product Based on MODIS Data Using a Deep Convolution Network Approach

Downscaling is a very important process to convert a coarse domain satellite product to a finer spatial resolution. In this paper, a deep learning based downscaling method was designed to improve the spatial resolution of the global sea surface salinity (SSS) products of Soil Moisture and Ocean Salinity (SMOS) satellite. The proposed algorithm is able to efficiently and effectively use high spatial-resolution Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data to improve the spatial resolution of SMOS SSS products.

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Proceedings of the 2019 3rd International Conference on Advances in Image Processing

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