DDUNet: Dual Dynamic U-Net for Highly-Efficient Cloud Segmentation
Journal:
arXiv
Published Date:
Jan 26, 2025
Abstract
Cloud segmentation amounts to separating cloud pixels from non-cloud pixels
in an image. Current deep learning methods for cloud segmentation suffer from
three issues. (a) Constrain on their receptive field due to the fixed size of
the convolution kernel. (b) Lack of robustness towards different scenarios. (c)
Requirement of a large number of parameters and limitations for real-time
implementation. To address these issues, we propose a Dual Dynamic U-Net
(DDUNet) for supervised cloud segmentation. The DDUNet adheres to a U-Net
architecture and integrates two crucial modules: the dynamic multi-scale
convolution (DMSC), improving merging features under different reception
fields, and the dynamic weights and bias generator (DWBG) in classification
layers to enhance generalization ability. More importantly, owing to the use of
depth-wise convolution, the DDUNet is a lightweight network that can achieve
95.3% accuracy on the SWINySEG dataset with only 0.33M parameters, and achieve
superior performance over three different configurations of the SWINySEg
dataset in both accuracy and efficiency.
