An Automatic Cloud Detection Neural Network for High-Resolution Remote Sensing Imagery With Cloud-Snow Coexistence

Cloud detection is a crucial procedure in remote sensing preprocessing. However, cloud detection is challenging in cloud-snow coexisting areas because cloud and snow have a similar spectral characteristic in visible spectrum. To overcome this challenge, we presented an automatic cloud detection neural network (ACD net) integrated remote sensing imagery with geospatial data and aimed to improve the accuracy of cloud detection from high-resolution imagery under cloud-snow coexistence. The proposed ACD net consisted of two parts: 1) feature extraction networks and 2) cloud boundary refinement module. The feature extraction networks module was designed to extract the spectral-spatial and geographic semantic information of cloud from remote sensing imagery and geospatial data. The cloud boundary refinement module is used to further improve the accuracy of cloud detection. The results showed that the proposed ACD net can provide a reliably cloud detection result in cloud-snow coexistence scene. Compared with the state-of-the-art deep learning algorithms, the proposed ACD net yielded substantially higher overall accuracy of 97.92%. This letter provides a new approach to how remote sensing imagery and geospatial big data can be integrated to obtain high accuracy of cloud detection in the circumstance of cloud-snow coexistence.

Automated summary

Cloud detection is crucial in remote sensing, but challenging in cloud-snow coexisting areas. The proposed ACD net integrates remote sensing imagery and geospatial data to improve accuracy in cloud detection under cloud-snow coexistence.