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.
Highlights
Domestic and foreign studies on night lighting data mainly focus on social-economic estimation and urban research direction, Night lighting data has a good interdisciplinary type, and it provides the possibility to explore new research fields [1].To solve the salt noise filter problem of high-resolution image classification based on traditional pixel method, in recent years, object-oriented classification has become the main means of highresolution remote sensing image classification [2]
It is concluded that under the same conditions, the overall classification accuracy of U-Net is the highest, full convolutional network (FCN) is on 2nd and support vector machine (SVM) is in the 3rd place
We found both the calculated results of the semantic segmentation network models of the deep learning algorithm have more accuracy than the SVM algorithm results in the data released by the bureau of a statistic of Zhengzhou
Summary
Domestic and foreign studies on night lighting data mainly focus on social-economic estimation and urban research direction, Night lighting data has a good interdisciplinary type, and it provides the possibility to explore new research fields [1].To solve the salt noise filter problem of high-resolution image classification based on traditional pixel method, in recent years, object-oriented classification has become the main means of highresolution remote sensing image classification [2]. Xie et al updates and traces urban changesets using object-based thresholding to detect large-scale urban change (Xie Y et al, 2017) by enhancing the time series of DMSP / OLS night light (NTL) data. Aiming at night lighting data, this project selects the support vector machine (SVM) algorithm to extract an urban built-up area based on night lighting data. The extracted urban built-up areas are compared and validated with Landsat 8 high-resolution data, and the area of urban built-up areas obtained by the SVM algorithm is corrected and analyzed (Chen Z et al, 2017). Chen et al used the intensity of the night light (NTL) recorded by satellite sensors to identify the city center successfully by developing a local contour tree method, and demarcated the corresponding boundaries to determine their spatial relationship with the Shanghai metropolitan area [3]
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