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http://dx.doi.org/10.17820/eri.2021.8.4.253

Application and Evaluation of the Attention U-Net Using UAV Imagery for Corn Cultivation Field Extraction  

Shin, Hyoung Sub (Corp. Environment Remotesensing Institute (ERI))
Song, Seok Ho (Corp. Environment Remotesensing Institute (ERI))
Lee, Dong Ho (Department of Agricultural and Rural Engineering, Chungbuk National University)
Park, Jong Hwa (Department of Agricultural and Rural Engineering, Chungbuk National University)
Publication Information
Ecology and Resilient Infrastructure / v.8, no.4, 2021 , pp. 253-265 More about this Journal
Abstract
In this study, crop cultivation filed was extracted by using Unmanned Aerial Vehicle (UAV) imagery and deep learning models to overcome the limitations of satellite imagery and to contribute to the technological development of understanding the status of crop cultivation field. The study area was set around Chungbuk Goesan-gun Gammul-myeon Yidam-li and orthogonal images of the area were acquired by using UAV images. In addition, study data for deep learning models was collected by using Farm Map that modified by fieldwork. The Attention U-Net was used as a deep learning model to extract feature of UAV in this study. After the model learning process, the performance evaluation of the model for corn cultivation extraction was performed using non-learning data. We present the model's performance using precision, recall, and F1-score; the metrics show 0.94, 0.96, and 0.92, respectively. This study proved that the method is an effective methodology of extracting corn cultivation field, also presented the potential applicability for other crops.
Keywords
Unmanned Aerial Vehicle; Attention U-Net; Corn; Cultivation Field Extraction;
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