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http://dx.doi.org/10.7780/kjrs.2021.37.6.1.16

Change Detection of Building Objects in Urban Area by Using Transfer Learning  

Mo, Jun-sang (Department of Civil Engineering, Chungbuk National University)
Seong, Seon-kyeong (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.6_1, 2021 , pp. 1685-1695 More about this Journal
Abstract
To generate a deep learning model with high performance, a large training dataset should be required. However, it requires a lot of time and cost to generate a large training dataset in remote sensing. Therefore, the importance of transfer learning of deep learning model using a small dataset have been increased. In this paper, we performed transfer learning of trained model based on open datasets by using orthoimages and digital maps to detect changes of building objects in multitemporal orthoimages. For this, an initial training was performed on open dataset for change detection through the HRNet-v2 model, and transfer learning was performed on dataset by orthoimages and digital maps. To analyze the effect of transfer learning, change detection results of various deep learning models including deep learning model by transfer learning were evaluated at two test sites. In the experiments, results by transfer learning represented best accuracy, compared to those by other deep learning models. Therefore, it was confirmed that the problem of insufficient training dataset could be solved by using transfer learning, and the change detection algorithm could be effectively applied to various remote sensed imagery.
Keywords
Deep Learning; Remote Sensing; Change Detection; Transfer Learning;
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