Cloudformer V2: Set Prior Prediction and Binary Mask Weighted Network for Cloud Detection

Zheng Zhang, Zhiwei Xu, Chang’an Liu, Qing Tian and Yongsheng Zhou
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Zheng Zhang: School of Information Science and Technology, North China University of Technology, Beijing 100144, China
Zhiwei Xu: School of Information Science and Technology, North China University of Technology, Beijing 100144, China
Chang’an Liu: School of Information Science and Technology, North China University of Technology, Beijing 100144, China
Qing Tian: School of Information Science and Technology, North China University of Technology, Beijing 100144, China
Yongsheng Zhou: College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China

Mathematics, 2022, vol. 10, issue 15, 1-12

Abstract: Cloud detection is an essential step in optical remote sensing data processing. With the development of deep learning technology, cloud detection methods have made remarkable progress. Among them, researchers have started to try to introduce Transformer into cloud detection tasks due to its excellent performance in image semantic segmentation tasks. However, the current Transformer-based methods suffer from training difficulty and low detection accuracy of small clouds. To solve these problems, this paper proposes Cloudformer V2 based on the previously proposed Cloudformer. For the training difficulty, Cloudformer V2 uses Set Attention Block to extract intermediate features as Set Prior Prediction to participate in supervision, which enables the model to converge faster. For the detection of small clouds, Cloudformer V2 decodes the features by a multi-scale Transformer decoder, which uses multi-resolution features to improve the modeling accuracy. In addition, a binary mask weighted loss function (BW Loss) is designed to construct weights by counting pixels classified as clouds; thus, guiding the network to focus on features of small clouds and improving the overall detection accuracy. Cloudformer V2 is experimented on the dataset from GF-1 satellite and has excellent performance.

Keywords: cloud detection; remote-sensing images; transformer (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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