  Clouds are a very important factor in the availability of optical remote
sensing images. Recently, deep learning-based cloud detection methods have
surpassed classical methods based on rules and physical models of clouds.
However, most of these deep models are very large which limits their
applicability and explainability, while other models do not make use of the
full spectral information in multi-spectral images such as Sentinel-2. In this
paper, we propose a lightweight network for cloud detection, fusing multi-scale
spectral and spatial features (CDFM3SF) and tailored for processing all
spectral bands in Sentinel- 2A images. The proposed method consists of an
encoder and a decoder. In the encoder, three input branches are designed to
handle spectral bands at their native resolution and extract multiscale
spectral features. Three novel components are designed: a mixed depth-wise
separable convolution (MDSC) and a shared and dilated residual block (SDRB) to
extract multi-scale spatial features, and a concatenation and sum (CS)
operation to fuse multi-scale spectral and spatial features with little
calculation and no additional parameters. The decoder of CD-FM3SF outputs three
cloud masks at the same resolution as input bands to enhance the supervision
information of small, middle and large clouds. To validate the performance of
the proposed method, we manually labeled 36 Sentinel-2A scenes evenly
distributed over mainland China. The experiment results demonstrate that
CD-FM3SF outperforms traditional cloud detection methods and state-of-theart
deep learning-based methods in both accuracy and speed.
