• 遥感科学与应用技术 •

### 集成夜间灯光数据与Landsat TM影像的不透水面自动提取方法研究

1. 1. 成都理工大学地球物理学院,成都 610059
2. 浙江工业大学计算机与技术学院,杭州 3100143. 中国科学院遥感与数字地球研究所,北京 100101
4. 中国科学院大学,北京 100101
• 收稿日期:2017-06-30 修回日期:2017-08-31 出版日期:2017-10-20 发布日期:2017-10-20
• 作者简介:

作者简介：程 熙（1982-）,男,四川乐山人,博士,研究方向为遥感信息提取。E-mail: chengxi13@cdut.cn

• 基金资助:
国家重点研发项目（2017YFB0504204、2016YFB0502502）;国家自然科学基金项目（41301488）

### Automatic Extraction Method for Impervious Surface Area by Integrating Nighttime light Data and Landsat TM Images

CHENG Xi1,*(), WU Wei2, XIA Liegang2, LUO Rui1, SHEN Zhanfeng3

1. 1. College of Geophysics, Chengdu University of Technology, Chengdu 610059, China
2. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310014, China
3. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
4. University of Chinese Academy of Sciences, Beijing 100101, China
• Received:2017-06-30 Revised:2017-08-31 Online:2017-10-20 Published:2017-10-20
• Contact: CHENG Xi E-mail:chengxi13@cdut.cn

Abstract:

Using multi-source remote sensing data to extract impervious surface information is an important and active research direction. The present study integrated spatial and spectral information from nighttime light data and Landsat TM remote sensing images to automatically extract the coverage information of Impervious Surface Area (ISA), given that in the previous studies, manual selection of impervious surface samples was usually needed for model training. In the present method, firstly, ISA concentrated urban areas were located according to the distribution of nighttime lights. Thus, the ISA spectral characteristics of the local scale in the urban area parts were more clear and obvious compared to the whole-image scene scale. Meanwhile, for the urban exterior, there were mostly non-ISA pixels, therefore the soil samples which were easily confused with ISA were extracted from the urban exterior, and the general spectral features of these samples on this image were calculated. These features could be utilized to distinguish ISA pixels from urban areas. Thus, highly reliable ISA and non-ISA samples were automatically selected from urban area and urban exterior, respectively. Secondly, ISA from urban areas was extracted by an iterative classification process. For the iterative classification process, new samples from the previous extraction results were collected and then added to the following classification process, to make the features of the ISA samples more representative of different types of ISA coverage. Then, ISA samples of urban area were selected from the extraction results, combined with the non-ISA samples of the urban exterior. A sample set was formed to classify the urban exterior. Lastly, the classification results were integrated to complete the whole image. An experiment with this method was completed. DMSP/OLS nighttime light images and Landsat5 TM images of the Syracuse, USA were chosed. 84 urban areas were extracted and the detection accuracy rate was above 95% compared to the Openstreet map. Two urban areas with high and low ISA density from the detection results were selected as the test areas. Automatic selection of ISA and non-ISA samples were performed to the TC transform feature bands of the Landsat5 TM images. The overall accuracy and kappa coefficients of sample selection in urban areas were 92.45% and 0.76, respectively, and 96.52% and 0.85 in urban exterior. For the results extracted by decision tree classifier, the average overall accuracy and Kappa coefficient were 88.23% and 0.63 in the urban areas; 78.6% and 0.54 in the urban exterior. These results are superior to manual methods. This is because the approach of automatic samples selection was more capable of obtaining samples of mixed pixel types compared to manual samples selection. Moreover, the representativeness of samples in spatial distribution and spectral characteristics was better since the iterative classification process was introduced. It suggests an automated classifcaltion workflow is achieved by the proposed method, and this method is reliable and effective for both urban area and urban exterior. In further researches, it could be expected that the ISA extraction accuracy could be improved by optimizing classification characteristics (e.g. adding space features) and improving classification algorithms.