Cloud and Cloud Shadow Detection Tests Based on Multiscale Feature Fusion Network

Cloud detection based on high-resolution optical images combined with deep learning methodology cannot provide adequate and accurate information about the cloud, cloud shadows, or their edge details. The main reason for this problem is the insufficient fusion of semantic information in different scales of classification techniques. To address this problem, this study combines the Res.block (Residual block) module that can prevent network degradation, multiscale convolution module that can increase the receptive field of the network, and multiscale feature module that can extract and integrate information from different scales. In addition, this study proposes a detection algorithm based on the multiscale feature fusion network and deep learning. The experimental results showed that rich spatial and semantic information could be extracted by the algorithm. Cloud and cloud shadow masks with a higher level of accuracy can also be acquired. The accuracy of cloud and cloud shadow detection is 0.9351 and 0.8103, respectively. This study provides theoretical support and technical reserve for the application of deep learning techniques to operational cloud detection.