A transformer-based siamese network using a self-attention mechanism for change detection in remote sensing data

Abstract

Efficient change detection in remote sensing data remains a critical challenge, necessitating a comprehensive solution that addresses diverse data formats and applications. Existing methods often specialize in specific complications, lacking a unified approach for various data types and resolutions. In this study, we propose an innovative methodology that integrates a Siamese network with a Transformer model using a self-attention mechanism, providing a versatile solution for change detection. Our approach introduces a Pairwise Learning Task and Feature Fusion techniques, leveraging Convolutional and Transformer Layers to enhance precision. The Siamese network, developed for similarity determination, is augmented with self-attention mechanisms, enabling the capture of intricate feature relationships across sequences. Our method not only excels in binary change mapping but also in feature type identification, adding valuable insights to the remote sensing domain. Experimental results on Sentinel-2, QuickBird, and TerraSAR-X datasets demonstrate that our approach achieves overall accuracies of 99.23 %, 98.96 %, and 99.08 %, respectively, and F1-scores of 92.16 %, 95.74 %, and 96.21 %, significantly outperforming existing state-of-the-art methods by margins of up to 4.3 % in accuracy and 7.7 % in F1-score. These results highlight the proposed method’s adaptability, precision, and robustness. The methodology, focusing on efficiency and accuracy, presents a significant advancement in remote sensing change detection, with promising applications in environmental monitoring, urban planning, and disaster management.