热带气旋
地球静止轨道
遥感
分割
计算机科学
图像分割
云计算
气象学
计算机视觉
人工智能
环境科学
地质学
气候学
地理
卫星
天文
物理
操作系统
作者
Joshua May,Liang Hu,Elizabeth A. Ritchie,Mehrtash Harandi,J. Scott Tyo
出处
期刊:IEEE Geoscience and Remote Sensing Letters
[Institute of Electrical and Electronics Engineers]
日期:2024-01-01
卷期号:21: 1-5
标识
DOI:10.1109/lgrs.2024.3358733
摘要
We demonstrate that a convolutional neural network (CNN) based on the U-Net architecture can be used to create a cloud mask data set that accurately identifies the clouds associated with tropical cyclones (TCs). The CNN can be trained using a single year of cloud masks produced by an earlier first-principles algorithm, and the results are insensitive to the specific year of training data used. These masks were originally created in order to compute the upwelling radiation due to TC clouds, and we show that the predicted masks result in both pixel areas and radiation calculations that are nearly identical to those computed using the earlier masks.
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