A Novel Ground-Based Cloud Image Segmentation Method Based on a Multibranch Asymmetric Convolution Module and Attention Mechanism

A Novel Ground-Based Cloud Image Segmentation Method Based on a Multibranch Asymmetric Convolution Module and Attention Mechanism

Cloud segmentation is a fundamental step in accurately acquiring cloud cover. However, due to the nonrigid structures of clouds, traditional cloud segmentation methods perform worse than expected. In this paper, a novel deep convolutional neural network (CNN) named MA-SegCloud is proposed for segmen...

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Bibliographic Details
Main Authors: Liwen Zhang, Wenhao Wei, Bo Qiu, Ali Luo, Mingru Zhang, Xiaotong Li
Format: Article
Language:English
Published: MDPI AG 2022-08-01
Series:Remote Sensing
Subjects:
ground-based cloud image segmentation
convolutional block attention module (CBAM)
squeeze-and-excitation module (SEM)
multibranch asymmetric convolution module (MACM)
whole sky imager (WSI)
Online Access:https://www.mdpi.com/2072-4292/14/16/3970

Internet

https://www.mdpi.com/2072-4292/14/16/3970

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