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http://dx.doi.org/10.11108/kagis.2020.23.3.252

Land Cover Classification Based on High Resolution KOMPSAT-3 Satellite Imagery Using Deep Neural Network Model  

MOON, Gab-Su (Geospatial Research Center, GEO C&I., Ltd.)
KIM, Kyoung-Seop (Geospatial Research Center, GEO C&I., Ltd.)
CHOUNG, Yun-Jae (Geospatial Research Center, GEO C&I., Ltd.)
Publication Information
Journal of the Korean Association of Geographic Information Studies / v.23, no.3, 2020 , pp. 252-262 More about this Journal
Abstract
In Remote Sensing, a machine learning based SVM model is typically utilized for land cover classification. And study using neural network models is also being carried out continuously. But study using high-resolution imagery of KOMPSAT is insufficient. Therefore, the purpose of this study is to assess the accuracy of land cover classification by neural network models using high-resolution KOMPSAT-3 satellite imagery. After acquiring satellite imagery of coastal areas near Gyeongju City, training data were produced. And land cover was classified with the SVM, ANN and DNN models for the three items of water, vegetation and land. Then, the accuracy of the classification results was quantitatively assessed through error matrix: the result using DNN model showed the best with 92.0% accuracy. It is necessary to supplement the training data through future multi-temporal satellite imagery, and to carry out classifications for various items.
Keywords
High Resolution KOMPSAT-3 Satellite Imagery; Land Cover Classification; Support Vector Machine(SVM); Artificial Neural Network(ANN); Deep Neural Network(DNN);
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Times Cited By KSCI : 11  (Citation Analysis)
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