Qi, Y.; Jia, Z.; Song, Y.; Yang, S.; Zhu, J.; Liu, X.; Zheng, H. A Semi-Supervised Remote Sensing Image Change Detection Network Based on Consistent Regularization for Multi-Scale feature Fusion and Multiplexing. Preprints2024, 2024101886. https://doi.org/10.20944/preprints202410.1886.v1
APA Style
Qi, Y., Jia, Z., Song, Y., Yang, S., Zhu, J., Liu, X., & Zheng, H. (2024). A Semi-Supervised Remote Sensing Image Change Detection Network Based on Consistent Regularization for Multi-Scale feature Fusion and Multiplexing. Preprints. https://doi.org/10.20944/preprints202410.1886.v1
Chicago/Turabian Style
Qi, Y., Xin Liu and Hongxing Zheng. 2024 "A Semi-Supervised Remote Sensing Image Change Detection Network Based on Consistent Regularization for Multi-Scale feature Fusion and Multiplexing" Preprints. https://doi.org/10.20944/preprints202410.1886.v1
Abstract
Remote sensing image change detection is a key application technology in remote sensing. In recent years, the development of deep learning has brought new opportunities for remote sensing image change detection. However, to achieve high change detection accuracy, large-scale, good-quality labeled data is required to train the model, which is often difficult to obtain and costly. This paper proposes a semi-supervised remote sensing image change detection network based on consistent regularization for multi-scale feature fusion and reuse (SCMFM-CDNet), which can enhance change detection performance with limited labeled data. Specifically, the SCMFM-CDNet consists of two parts, the supervised stage multi-scale feature fusion and reuse network (MFM-CDNet) and the consistency regularization method of semi-supervised learning. The MFM-CDNet network uses channel attention mechanism and spatial attention mechanism to enhance the expression ability of features, and gradually completes the extraction and detection of change features through multi-scale feature fusion and reuse strategy. The consistency regularization method of semi-supervised learning adopts the teacher model and student model for alternate training, updating model parameters by minimizing the differences in their predictions, thus achieving semi-supervised learning. This paper conducted extensive experiments on three public datasets, LEVIR-CD, WHU-CD, and GoogleGZ-CD. Under the three labeling data ratios of 5%, 10%, and 20%, the F1 scores of the SCMFM-CDNet network on the LEVIR-CD dataset were 88.62, 90.78, and 90.63, respectively. On the WHU-CD dataset, the F1 scores were 88.40, 90.17, and 90.83, respectively. On the GoogleGZ-CD dataset, the F1 scores were 81.44, 82.36, and 85.40, respectively. The results indicate that the proposed network outperforms existing semi-supervised change detection methods in terms of comprehensive performance. In addition, this paper also conducted ablation experiments to evaluate the contribution of the global context module (GCM) and feature fusion reuse module (FFM) in the MFM-CDNet network. The experimental results show that these two modules are crucial for improving network performance.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
