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

Quan Qi; Yuhua Wang

doi:10.54097/jvkz4w09

Authors

Quan Qi
Yuhua Wang

DOI:

https://doi.org/10.54097/jvkz4w09

Keywords:

Remote sensing technology; combined pixel level; remote sensing image.

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.

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References

[1] WANG Q, ZHANG X, CHEN G, et al. Change detection based on Faster R-CNN for high-resolution remote sensing images [J]. Remote Sensing Letters, 2018. DOI: https://doi.org/10.1080/2150704X.2018.1492172

[2] WANG M, ZHANG H, SUN W, et al. A Coarse-to-Fine Deep Learning Based Land Use Change Detection Method for High-Resolution Remote Sensing Images [J]. Remote Sensing, 2020. DOI: https://doi.org/10.3390/rs12121933

[3] XING, HUAQIAO, ZHU, et al. A comparative study of threshold selection methods for change detection from very high-resolution remote sensing images [J]. Earth Science Informatics, 2022. DOI: https://doi.org/10.1007/s12145-021-00734-y

[4] HUANG L, AN R, ZHAO S, et al. A Deep Learning-Based Robust Change Detection Approach for Very High Resolution Remotely Sensed Images with Multiple Features [J]. Remote Sensing, 2020. DOI: https://doi.org/10.3390/rs12091441

[5] LARABI M E A, CHAIB S, BAKHTI K, et al. High-resolution optical remote sensing imagery change detection through deep transfer learning [J]. Journal of Applied Remote Sensing, 2019. DOI: https://doi.org/10.1117/1.JRS.13.046512

[6] WANG H, QI J, LEI Y, et al. A Refined Method of High-Resolution Remote Sensing Change Detection Based on Machine Learning for Newly Constructed Building Areas [J]. Remote Sensing, 2021. DOI: https://doi.org/10.3390/rs13081507

[7] JIANG H, PENG M, ZHONG Y, et al. A Survey on Deep Learning-Based Change Detection from High-Resolution Remote Sensing Images [J]. Remote Sensing, 2022. DOI: https://doi.org/10.3390/rs14071552