Reconstructing Geostationary Satellite Land Surface Temperature Imagery Based on a Multiscale Feature Connected Convolutional Neural Network

Reconstructing Geostationary Satellite Land Surface Temperature Imagery Based on a Multiscale Feature Connected Convolutional Neural Network

Geostationary satellite land surface temperature (GLST) data are important for various dynamic environmental and natural resource applications for terrestrial ecosystems. Due to clouds, shadows, and other atmospheric conditions, the derived LSTs are often missing a large number of values. Reconstruc...

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Bibliographic Details
Main Authors: Penghai Wu, Zhixiang Yin, Hui Yang, Yanlan Wu, Xiaoshuang Ma
Format: Article
Language:English
Published: MDPI AG 2019-02-01
Series:Remote Sensing
Subjects:
geostationary satellite land surface temperature (GLST)
convolutional neural networks (CNN)
multiscale feature connection
large missing regions
reconstruction
Online Access:https://www.mdpi.com/2072-4292/11/3/300

Internet

https://www.mdpi.com/2072-4292/11/3/300

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