Publications

Ge, WX; Yang, XB; Jiang, R; Shao, W; Zhang, L (2024). CD-CTFM: A Lightweight CNN-Transformer Network for Remote Sensing Cloud Detection Fusing Multiscale Features. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 17, 4538-4551.

Abstract
Clouds in remote sensing images inevitably affect information extraction, which hinders the following analysis of satellite images. Hence, cloud detection is a necessary preprocessing procedure. However, most existing methods have numerous calculations and parameters. In this article, a lightweight convolutional neural network (CNN)-Transformer network, CD-CTFM, is proposed to solve the problem, which is based on encoder-decoder architecture and incorporates the attention mechanism. In the encoder part, we utilize a lightweight network combing CNN and Transformer as backbone, which is conducive to extracting local and global features simultaneously. The backbone of CD-CTFM also incorporates attention gate based on dark channel extraction module. Moreover, a lightweight feature pyramid module is designed to fuse multiscale features with contextual information. In the decoder part, a lightweight channel-spatial attention module is integrated into each skip connection between encoder and decoder to extract low-level features while suppressing irrelevant information without introducing many parameters. Finally, the proposed model is evaluated on two cloud datasets, 38-Cloud and MODIS. The results demonstrate that CD-CTFM achieves comparable accuracy as the state-of-art methods and outperforms in terms of efficiency.

DOI:
10.1109/JSTARS.2024.3361933

ISSN:
2151-1535