Research on Ground-Based Cloud Image Segmentation Based on Deep Learning
ground-based cloud segmentation is susceptible to various environmental factors such as weather conditions,lighting,and solar zenith angle.Existing deep learning segmentation methods often yield unsatisfac tory results in cloud boundary segmentation without domain adaptation.Considering these factors,this study selects the CloudSegNet,DeepLabV3,and U-Net models known for their strong boundary recognition capabili ties.Furthermore,to identify the optimal feature extraction networks,VGG19,ResNet101,SE_Resnext101,and Mobilenet_v2 are chosen through research.Lastly,to further enhance the segmentation capability of cloud boundaries,multi-task learning is introduced on top of existing deep segmentation models to independently model cloud layer boundaries and improve the recognition ability of cloud layer boundaries.
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