CD-CTFM: A Lightweight CNN-Transformer Network for Remote Sensing Cloud
  Detection Fusing Multiscale Features
Wenxuan Ge Xubing Yang Li Zhang
Clouds in remote sensing images inevitably affect information extraction,
which hinder the following analysis of satellite images. Hence, cloud detection
is a necessary preprocessing procedure. However, the existing methods have
numerous calculations and parameters. In this letter, a lightweight
CNN-Transformer network, CD-CTFM, is proposed to solve the problem. CD-CTFM is
based on encoder-decoder architecture and incorporates the attention mechanism.
In the decoder part, we utilize a lightweight network combing CNN and
Transformer as backbone, which is conducive to extract local and global
features simultaneously. Moreover, a lightweight feature pyramid module is
designed to fuse multiscale features with contextual information. In the
decoder part, we integrate a lightweight channel-spatial attention module into
each skip connection between encoder and decoder, extracting 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. At the same time, CD-CTFM outperforms state-of-art
methods in terms of efficiency.