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http://dx.doi.org/10.7780/kjrs.2020.36.6.2.7

KOMPSAT-3A Urban Classification Using Machine Learning Algorithm - Focusing on Yang-jae in Seoul -  

Youn, Hyoungjin (Department of Geoinformatics Engineering, Namseoul University)
Jeong, Jongchul (Department of Geoinformatics Engineering, Namseoul University)
Publication Information
Korean Journal of Remote Sensing / v.36, no.6_2, 2020 , pp. 1567-1577 More about this Journal
Abstract
Urban land cover classification is role in urban planning and management. So, it's important to improve classification accuracy on urban location. In this paper, machine learning model, Support Vector Machine (SVM) and Artificial Neural Network (ANN) are proposed for urban land cover classification based on high resolution satellite imagery (KOMPSAT-3A). Satellite image was trained based on 25 m rectangle grid to create training data, and training models used for classifying test area. During the validation process, we presented confusion matrix for each result with 250 Ground Truth Points (GTP). Of the four SVM kernels and the two activation functions ANN, the SVM Polynomial kernel model had the highest accuracy of 86%. In the process of comparing the SVM and ANN using GTP, the SVM model was more effective than the ANN model for KOMPSAT-3A classification. Among the four classes (building, road, vegetation, and bare-soil), building class showed the lowest classification accuracy due to the shadow caused by the high rise building.
Keywords
Machine Learning; KOMPSAT-3A; Land-cover; Support Vector Machine; Artificial Neural Network;
Citations & Related Records
Times Cited By KSCI : 16  (Citation Analysis)
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