  Cloud detection in satellite images is an important first-step in many remote
sensing applications. This problem is more challenging when only a limited
number of spectral bands are available. To address this problem, a deep
learning-based algorithm is proposed in this paper. This algorithm consists of
a Fully Convolutional Network (FCN) that is trained by multiple patches of
Landsat 8 images. This network, which is called Cloud-Net, is capable of
capturing global and local cloud features in an image using its convolutional
blocks. Since the proposed method is an end-to-end solution, no complicated
pre-processing step is required. Our experimental results prove that the
proposed method outperforms the state-of-the-art method over a benchmark
dataset by 8.7\% in Jaccard Index.
