Abstract
ABSTRACT In the application of remote sensing, cloud blocking brings trouble to the analysis of surface parameters and atmospheric parameters. Due to the complexity of the background, the influence of some cloud-like interferences (such as ice, snow, buildings, etc.) and the complexity of the cloud shape, the traditional deep learning method is difficult to segment the edge information of cloud and cloud shadow accurately, resulting in misjudgement at the edge. In order to solve these problems, a multilevel feature enhanced network is proposed for cloud/shadow segmentation. In this work, ResNet-18 is used as the backbone to extract all levels of semantic information, and Feature Enhancement Module is proposed to strengthen the feature information to obtain more effective feature information. Multiscale Fusion module is constructed to fuses multiscale features of deep information to obtain global feature information while considering local feature information. Finally, through the Feature Guidance module, low-level features are used to guide the high-level features to guide the recovery of spatial information and improve the efficiency of upsampling. On the data collected by Landsat-8, Sentinel-2, and HRC_WHU data set, the experimental results show that this method is superior to the existing methods.
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