An All-Scale Feature Fusion Network With Boundary Point Prediction for Cloud Detection
Wenjing Wang, Zhenwei Shi
Abstract
Cloud detection is a significant pre-processing for remote sensing images. In recent years, many methods based on deep learning are proposed to detect clouds and multi-scale feature fusion is often used in these methods. However, most existing methods fuse features through concatenation and element-wise summation, which are simple and can be improved in spatial information recovery. Therefore, we explore the way of fusing features to recover the missing spatial information more sufficiently. Besides, we also observe that some cloud detection results are not accurate enough near the boundary of clouds. In view of the above observations, in this letter, we propose a cloud detection network, ABNet, which includes All-scale feature Fusion modules and a Boundary point Prediction module. The All-scale feature Fusion module can optimize the features and recover spatial information by integrating features of all scales. And the Boundary point Prediction module further remedies cloud boundary information by classifying the cloud boundary points separately. Experimental results demonstrate that our method improves the accuracy of cloud detection compared with other methods.