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http://dx.doi.org/10.7780/kjrs.2021.37.5.1.20

Availability Evaluation of Object Detection Based on Deep Learning Method by Using Multitemporal and Multisensor Data for Nuclear Activity Analysis  

Seong, Seon-kyeong (Department of Civil Engineering, Chungbuk National University)
Choi, Ho-seong (Korea Institute of Nuclear Nonproliferation and Control)
Mo, Jun-sang (Department of Civil Engineering, Chungbuk National University)
Choi, Jae-wan (Department of Civil Engineering, Chungbuk National University)
Publication Information
Korean Journal of Remote Sensing / v.37, no.5_1, 2021 , pp. 1083-1094 More about this Journal
Abstract
In order to monitor nuclear activity in inaccessible areas, it is necessary to establish a methodology to analyze changesin nuclear activity-related objects using high-resolution satellite images. However, traditional object detection and change detection techniques using satellite images have difficulties in applying detection results to various fields because effects of seasons and weather at the time of image acquisition. Therefore, in this paper, an object of interest was detected in a satellite image using a deep learning model, and object changes in the satellite image were analyzed based on object detection results. An initial training of the deep learning model was performed using an open dataset for object detection, and additional training dataset for the region of interest were generated and applied to transfer learning. After detecting objects by multitemporal and multisensory satellite images, we tried to detect changes in objects in the images by using them. In the experiments, it was confirmed that the object detection results of various satellite images can be directly used for change detection for nuclear activity-related monitoring in inaccessible areas.
Keywords
Deep Learning; Object Detection; Change Detection;
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1 Girshick, R., J. Donahue, T. Darrell, and J. Malik, 2014. Rich feature hierarchies for accurate object detection and semantic segmentation, Proc. of the IEEE conference on computer vision and pattern recognition, Columbus, OH, USA, Jun. 23-28, pp. 580-587.
2 Ophoff. T., S. Puttemans, V. Kalogirou, J-P. Pobin, and T. Goedem, 2020. Vehicle and vessel detection on satellite imagery: A comparative study on single-shot detectors, Remote Sensing, 12(7): 1217.   DOI
3 Park, H., 2009. A study on the acquisition scheme of North Korea geospatial information, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, 27(6): 749-760 (in Korean with English abstract).
4 Ren, S., K. He, R. Girshick, and J. Sun, 2017. Faster R-CNN: Towards real-time object detection with region proposal networks, IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6): 1137-1149   DOI
5 Shi, W., M. Zhang, R. Zhang, S. Chen, and Z. Zhan, 2020. Change detection based on artificial intelligence: State-of-the-art and challenges, Remote Sensing, 12(10): 1688.   DOI
6 Song, A., Y. Kim, and Y. Han, 2020. Uncertainty analysis for object-based change detection in very high-resolution satellite images using deep learning network, Remote Sensing, 12(15): 2345.   DOI
7 Song, C., W. Wahyu, J. Jung, S. Hong, D. Kim, and J. Kang, 2020. Urban change detection for high-resolution satellite images using U-Net based on SPADE, Korean Journal of Remote Sensing, 36(6): 1579-1590 (in Korean with English abstract).   DOI
8 Han, Q., Q. Yin, X. Zheng, and Z. Chen, 2021. Remote sensing image building detection method based on Mask R-CNN, Complex and Intelligent Systems, 2021: 1-9.   DOI
9 Bochkovskiy, A., C. Wang, and H. Liao, 2020. YOLOv4: Optimal speed and accuracy of object detection, Computer Vision and Pattern Recognition, arXiv preprint (arXiv): 2004.10934
10 Chen, Z., K. Lu, L. Gao, B. Li, J. Gao, X. Yang, M. Yao, and B. Zhang, 2019. Automatic detection of track and fields in China from high-resolution satellite images using multi-scale-fused single shot multibox detector, Remote Sensing, 11(11): 1377.   DOI
11 Redmon, J., S. Divvala, R. Girshick, and A. Farhadi, 2016. You only look once: Unified, real-time object detection, Proc. of the IEEE conference on computer vision and pattern recognition, Las Vegas, NV, USA, Jun. 27-30, pp. 779-788.
12 AIHub, 2021, Satellite image dataset, https://aihub.or.kr/aidata/7982, Accessed on Oct. 21, 2021.
13 Choi, J., H. Park, D. Kim, and S. Choi, 2018. Unsupervised change detection of KOMPSAT-3 satellite imagery based on cross-sharpened images by guided filter, Korean Journal of Remote Sensing, 34(5): 777-786 (in Korean with English abstract).   DOI
14 Kim, S. and H. Park, 2021. Evaluation of SWIR bands utilization of Worldview-3 satellite imagery for mineral detection, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, 39(3): 203-209 (in Korean with English abstract).   DOI
15 Sokolova, M., N. Japkowicz, and S. Szpakowicz, 2016. Beyond accuracy, F-score and ROC: a family of discriminant measures for performance evaluation, In Australasian joint conference on artificial intelligence, Springer, Berlin, Heidelberg, pp. 1015-1021.