Urban Change Detection by Fully Convolutional Siamese Concatenate Network with Attention

TL;DR

This paper introduces a novel fully automatic change detection method for very high-resolution remote sensing images using a Siamese network with attention mechanisms, improving accuracy in urban change detection tasks.

Contribution

The paper proposes a new FC-Siam-Conc network with integrated attention gate layers and Gaussian attention preprocessing for enhanced change detection in VHR remote sensing images.

Findings

Outperforms state-of-the-art algorithms on OSCD dataset

Effective use of attention mechanisms improves detection accuracy

Demonstrates robustness across different datasets

Abstract

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.