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http://dx.doi.org/10.7780/kjrs.2020.36.5.1.14

Assessment of the FC-DenseNet for Crop Cultivation Area Extraction by Using RapidEye Satellite Imagery  

Seong, Seon-kyeong (Department of Civil Engineering, Chungbuk National University)
Na, Sang-il (Climate Change Assessment Division, National Institute of Agricultural Sciences, Rural Development Administration)
Choi, Jae-wan (Department of Civil Engineering, Chungbuk National University)
Publication Information
Korean Journal of Remote Sensing / v.36, no.5_1, 2020 , pp. 823-833 More about this Journal
Abstract
In order to stably produce crops, there is an increasing demand for effective crop monitoring techniques in domestic agricultural areas. In this manuscript, a cultivation area extraction method by using deep learning model is developed, and then, applied to satellite imagery. Training dataset for crop cultivation areas were generated using RapidEye satellite images that include blue, green, red, red-edge, and NIR bands useful for vegetation and environmental analysis, and using this, we tried to estimate the crop cultivation area of onion and garlic by deep learning model. In order to training the model, atmospheric-corrected RapidEye satellite images were used, and then, a deep learning model using FC-DenseNet, which is one of the representative deep learning models for semantic segmentation, was created. The final crop cultivation area was determined as object-based data through combination with cadastral maps. As a result of the experiment, it was confirmed that the FC-DenseNet model learned using atmospheric-corrected training data can effectively detect crop cultivation areas.
Keywords
Crop cultivation area; Deep learning; RapidEye satellite imagery; FC-DenseNet; Cadastral Map;
Citations & Related Records
Times Cited By KSCI : 6  (Citation Analysis)
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