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http://dx.doi.org/10.7780/kjrs.2022.38.6.1.1

Change Detection Using Deep Learning Based Semantic Segmentation for Nuclear Activity Detection and Monitoring  

Song, Ahram (Department of Location-Based Information System, Kyungpook National University)
Lee, Changhui (Department of Civil Engineering, Seoul National University of Science and Technology,)
Lee, Jinmin (Department of Civil Engineering, Seoul National University of Science and Technology,)
Han, Youkyung (Department of Civil Engineering, Seoul National University of Science and Technology,)
Publication Information
Korean Journal of Remote Sensing / v.38, no.6_1, 2022 , pp. 991-1005 More about this Journal
Abstract
Satellite imaging is an effective supplementary data source for detecting and verifying nuclear activity. It is also highly beneficial in regions with limited access and information, such as nuclear installations. Time series analysis, in particular, can identify the process of preparing for the conduction of a nuclear experiment, such as relocating equipment or changing facilities. Differences in the semantic segmentation findings of time series photos were employed in this work to detect changes in meaningful items connected to nuclear activity. Building, road, and small object datasets made of KOMPSAT 3/3A photos given by AIHub were used to train deep learning models such as U-Net, PSPNet, and Attention U-Net. To pick relevant models for targets, many model parameters were adjusted. The final change detection was carried out by including object information into the first change detection, which was obtained as the difference in semantic segmentation findings. The experiment findings demonstrated that the suggested approach could effectively identify altered pixels. Although the suggested approach is dependent on the accuracy of semantic segmentation findings, it is envisaged that as the dataset for the region of interest grows in the future, so will the relevant scope of the proposed method.
Keywords
Nuclear activity; Change detection; Deep learning; Semantic segmentation; High spatial resolution satellite image;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
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