Gradual Boundary Net: A Gradual Boundary Attention Based Deep Learning Framework for Cloud Detection

Cloud detection is a basic and important task in many high level applications of remote sensing technology. Accurate cloud detection is a challenging task. On the one hand, clouds are normally exhibited at different sizes and thicknesses. On the other hand, the boundary between the clouds and their background is usually not sharp. To address the two challenges, we present a deep learning based strategy, i.e., Gradual Boundary Net, which generates a cloud mask for detecting clouds in one cloudy image. The Gradual Boundary Net consists of two stages: (a) coarse location stage and (b) gradual boundary refinement stage. At the coarse location stage, the feature extraction network with four encoders and a cascade partial decoder (CPD) is implemented to obtain the coarse score map for locating the clouds with different sizes and thicknesses roughly. At the gradual boundary refinement stage, the coarse score map is gradually refined by a erasing and fusion strategy with several gradual boundary attention modules (GBAMs). The refined cloud mask is obtained after the two stages. The experimental results validate that our Gradual Boundary Net performs well and achieves outstanding results. The code for implementing the proposed Gradual Boundary Net is available at https://github.com/kang-wu/Gradual-Boundary-Net.