LCDNet: Light-Weighted Cloud Detection Network for High-Resolution Remote Sensing Images

Abstract

Cloud detection is one of the important links in high-resolution remote sensing image processing. Cloud detection methods can be mainly divided into three categories: threshold methods, clustering methods based on machine learning, and deep learning methods. The traditional threshold method needs cumbersome manual calibration, which has high cost and poor universality. The generalization of clustering based method is very poor. In addition, existing deep learning methods tend to have many model parameters and high training costs. To solve the above problems, a light-weighted cloud detection network (LCDNet) based on deep learning method is proposed. The network can complete the task of high-precision segmentation with less parameters and computation. Its light-weighted bottleneck (LB) layer can quickly capture the multi-scale feature information in the image and segment the cloud with high efficiency. The gated channel excitation (GCE) module can effectively reduce the feature redundancy in the network, so as to highlight the details of the cloud layer and improve the detection accuracy. The function of light-weighted self attention (LSA) module is to quickly establish the spatial location information in the feature map, so as to locate the unpredictable targets and reduce the false detection rate. The experimental results show that the model shows excellent performance on cloud and cloud shadow datasets with 353.76 k parameters and 456.28Mmac computation. In addition, the training results on GF1_WHU and L8 Biome datasets further show that our model has excellent generalization performance, which is of great significance for the efficient implementation of cloud detection.