# Extraction of City Roads Using Luojia 1-01 Nighttime Light Data
## 3 Method
### Distinguishing of Urban Areas  
Due to the differences of light intensity of roads between urban area and rural areas, it was essential to distinguish the urban boundary of Wuhan. Although the administrative  
Figure 3: Flowchart of the proposed extraction approach.  
boundary could be obtained from the census data, some rural areas still existed within the administrative boundary.  
Different from Landsat data with several bands, Luojia 1-01 data only contained a single band of DN values. Therefore, we used a thresholding method to achieve the extraction, the principle of which was to find an optimal threshold intensity that could keep the major characteristics of urban-area pixels and eliminate rural-area pixels at the same time.  
The accuracy of this method highly relies on the choosing of a threshold intensity value. According to previous studies, the methods for finding out optimal threshold value can be distinguished into the following four categories [28; 39; 40; 41]: (1) Experience threshold method. In this kind of method, threshold value is determined according to expert knowledge, which is obtained through years of experience or a great deal of previous experiments. Though the advantage of this method is obvious--quick and simple, the over-reliance on historical knowledge also imitates its further use in a new field where the historical knowledge is lacking. (2) Statistical analysis method. In this kind of method, a number of candidate intensity values are chosen to extract the urban area of target city and the area of each extraction will be recorded. The optimal is the one that makes the area of extraction most close to the statistical data in the yearbook. However, the area information is mainly accurately measured in metropolitan cities, which limits the performing of this method in county-level and town-level cities. (3) Image comparison method. In this kind of method, external data sources, such as Landsat satellite images, Google Earth Images and MODIS images, through which the land use information can be derived, are adopted to match with nighttime light data and to help distinguish the boundary of an urban area. However, the method requires a mass of artificial participation to delineate the urban boundary, which can be high in cost and time consuming when performing analysis on multiple cities. (4) Mutation detection method. In this kind of method, the urban boundary is extracted according to the structure characteristics of an urban area. In a rural area, the perimeter of pixels, the intensity of which is larger than a certain value, will decrease with the increase in the value. However, the perimeter of urban-area pixels will show a fluctuating tendency with the increase in intensity value, causing the influence of polycentric features of an urban area. Additionally, the threshold value can be found through the fluctuating tendency, which can be described as follows [42]:  
\[f(N_{n})=i_{n}\times N_{n} \tag{6}\]  
\[\exists N_{m},\ \frac{df(N_{m-1})}{dN}\times\frac{df(N_{m+1})}{dN}\geq 0,\ (N_{m}=0,1,2,\ldots,N_{max}) \tag{7}\]  
\[N_{u}=\min(\forall N_{m}) \tag{8}\]  
where \(N_{m}\) represents the light intensity, ranging from 0 to the maximum value \(N_{max}\), and makes the function \(f(N)\) reach its minimal value. \(f(N)\) represents the function of urban perimeter and each light intensity value; \(i_{n}\) represents the number of pixels, the DN value of which is \(N_{n}\). There may exist several \(N_{m}\) values because of the complex city structure. To avoid the data loss, the minimum \(N_{m}\) was considered as the threshold value \(N_{u}\).