SCTRANS: A TRANSFORMER NETWORK BASED ON THE SPATIAL AND CHANNEL ATTENTION FOR CLOUD DETECTION

Wenke Jiao, Yongjun Zhang, Bin Zhang, Yi Wan

Abstract: Cloud detection is an important preprocessing step for remote sensing image processing and analysis. The current deep-learning-based cloud detection methods are mostly based on Convolutional Neural Network (CNN) which pay more attention to local information. To make more use of the global information, in this article, we propose a transformer-based cloud detection method (SCTrans) based on the spatial and channel attention mechanism. The experiment results show that when using only three-band images on the Landsat7 dataset, the mIoU of the validation set reaches 85.92% and the mIoU of the test set reaches 87.86%. The experimental results show that the proposed network has a higher mIoU and F1 score than Fmask and other networks.