Application and Optimization of Deep Learning in Change Detection for High-Resolution Remote Sensing Imagery

Abstract

With the continuous development of remote sensing technology and deep learning, change detection methods based on high-resolution remote sensing images are gradually evolving towards intelligence and high precision. Starting from the theoretical foundation of remote sensing image change detection, this paper systematically comprehends the technical framework and typical models of deep learning, and focuses on the analysis of its application modes in image alignment, feature extraction and bi-phasic analysis. In addition, the integration of multi-source remote sensing data and model adaptation are discussed with the idea of pixel-level and object-level modelling, which provides theoretical basis and methodological support for improving the accuracy and stability of change detection. The study shows that deep learning has a powerful characterisation capability and is an important development direction for change detection in remote sensing images in the future.