  Change detection (CD) is an important problem in remote sensing, especially
in disaster time for urban management. Most existing traditional methods for
change detection are categorized based on pixel or objects. Object-based models
are preferred to pixel-based methods for handling very high-resolution remote
sensing (VHR RS) images. Such methods can benefit from the ongoing research on
deep learning. In this paper, a fully automatic change-detection algorithm on
VHR RS images is proposed that deploys Fully Convolutional Siamese Concatenate
networks (FC-Siam-Conc). The proposed method uses preprocessing and an
attention gate layer to improve accuracy. Gaussian attention (GA) as a soft
visual attention mechanism is used for preprocessing. GA helps the network to
handle feature maps like biological visual systems. Since the GA parameters
cannot be adjusted during network training, an attention gate layer is
introduced to play the role of GA with parameters that can be tuned among other
network parameters. Experimental results obtained on Onera Satellite Change
Detection (OSCD) and RIVER-CD datasets confirm the superiority of the proposed
architecture over the state-of-the-art algorithms.
