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Research on Spatialization of Urban Area Based on Deep Learning

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DOI: 10.23977/autml.2020.020101 | Downloads: 10 | Views: 543

Author(s)

Ullah Inam 1,2, Weidong Li 1,2, Fanqian Meng 1,2

Affiliation(s)

1 Key Laboratory of Grain Information Processing and Control, Henan University of Technology, Ministry of Education , Zhengzhou 450001, China
2 College of Information Engineering, Henan University of Technology, Zhengzhou 450001, China

Corresponding Author

Weidong Li

ABSTRACT

This paper takes Zhengzhou City, Henan Province as the research area, NLD (Night Light Data) high-resolution remote sensing image of 2017 as the data source. Two different supervised algorithms (Support Vector Machine & Deep Learning) was used for classification. During Deep learning, two kinds of semantic segmentation network models are selected: FCN (Full Convolution Neural Network) model, and U-Net model to classify source data and analyze the effects of different semantic segmentation networks on classification accuracy. We calculate the urban area of 460.34 square kilometers, 447.28 square kilometers, and 452.57 square kilometers by SVM (Support Vector Machine) algorithm, U-Net model and FCN model, while the urban area of 437.60 square kilometers in 2018 was announced by Zhengzhou Municipal Bureau of Statistics. The results showed that the classification accuracy of the SVM algorithm is 95.06%, the U-Net model reached 97.83%, and the FCN model had 96.69%, under the same conditions and similar spectral information. We found that the U-Net network model can get better classification results in areas with serious mixed features, both the semantic segmentation network models of the deep learning algorithm are more accurate than the SVM algorithm to the data released by the bureau of a statistic of Zhengzhou.

KEYWORDS

Support Vector Machine; Deep learning, remote sensing; semantic segmentation network; classification

CITE THIS PAPER

Ullah Inam, Weidong Li and Fanqian Meng. Research on Spatialization of Urban Area Based on Deep Learning. Automation and Machine Learning(2020) Vol. 2: 1-10. DOI: http://dx.doi.org/10.23977/autml.2020.020101.

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