CloudDeepLabV3+: a lightweight ground-based cloud segmentation method based on multi-scale feature aggregation and multi-level attention feature enhancement

The segmentation of ground-based cloud images is the basis for obtaining numerous cloud parameters. To achieve high-precision adaptive cloud image segmentation requirements, this study designs a lightweight ground-based cloud image adaptive segmentation method named CloudDeepLabV3+ that integrates multi-scale features aggregation and multi-level attention feature enhancement. Firstly, a novel lightweight EfficientNetV2-S is designed as a feature extraction backbone to reduce network parameters. Secondly, a heterogeneous receptive field splicing atrous spatial pyramid pooling module is designed. It enhances the correlation of information between layers, and realizes multiscale information fusion. The feature enhancement module based on the self-attention mechanism intensifies the representation of local and global features. Thirdly, the feature alignment module based on the attention mechanism is constructed to pull deep and shallow features for alignment. Finally, we implement ablation study on the key components of method and comparison experiment with other advanced methods using five evaluation metrics. Results show that the key components play an important role in multiscale information fusion. It promotes the accuracy of cloud image feature extraction while reducing the loss of detailed features. Generalization performance verification indicates the excellent performance of the proposed model in advanced cloud feature extraction and cloud-mask generation.