Urban Change Detection by Fully Convolutional Siamese Concatenate Network with Attention
This work addresses the problem of monitoring urban changes for disaster management and urban planning, offering an incremental improvement through attention-based deep learning.
The paper tackled urban change detection in very high-resolution remote sensing images by proposing a fully automatic algorithm using a Fully Convolutional Siamese Concatenate network with attention mechanisms, achieving superior performance over state-of-the-art methods on OSCD and RIVER-CD datasets.
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.