  Cloud detection is an important preprocessing step for the precise
application of optical satellite imagery. In this paper, we propose a deep
learning based cloud detection method named multi-scale convolutional feature
fusion (MSCFF) for remote sensing images of different sensors. In the network
architecture of MSCFF, the symmetric encoder-decoder module, which provides
both local and global context by densifying feature maps with trainable
convolutional filter banks, is utilized to extract multi-scale and high-level
spatial features. The feature maps of multiple scales are then up-sampled and
concatenated, and a novel multi-scale feature fusion module is designed to fuse
the features of different scales for the output. The two output feature maps of
the network are cloud and cloud shadow maps, which are in turn fed to binary
classifiers outside the model to obtain the final cloud and cloud shadow mask.
The MSCFF method was validated on hundreds of globally distributed optical
satellite images, with spatial resolutions ranging from 0.5 to 50 m, including
Landsat-5/7/8, Gaofen-1/2/4, Sentinel-2, Ziyuan-3, CBERS-04, Huanjing-1, and
collected high-resolution images exported from Google Earth. The experimental
results show that MSCFF achieves a higher accuracy than the traditional
rule-based cloud detection methods and the state-of-the-art deep learning
models, especially in bright surface covered areas. The effectiveness of MSCFF
means that it has great promise for the practical application of cloud
detection for multiple types of medium and high-resolution remote sensing
images. Our established global high-resolution cloud detection validation
dataset has been made available online.
