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Automatic Extraction of Residential Information based on Object-oriented in the Areas around the Qinghai Lake |
Xihong Lian1,2(),Yuan Qi1(),Hongwei Wang1,Jinlong Zhang1,Rui Yang1,2 |
1.Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China 2.University of Chinese Academy of Sciences, Beijing 100049, China |
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Abstract The spatial pattern and density of residential areas directly reflect the intensity of regional human activities, and affect the evolution of a regional human-land system and the sustainable development of ecological environment. In this study, we proposed an objected-oriented automatic extraction method, which based on the high spatial resolution satellite remote sensing image data in the surrounding area of Qinghai lake watershed. Firstly, multi-scale segmentation of high-resolution satellite remote sensing image was carried out by using the scale sets theory to obtain segmentation objects in different scales. Secondly, the custom, spectral, geometric and texture features of the sample attributes were trained through the sets of machine learning algorithms, and the optimal automatic classification algorithm was selected. Finally, the optimal automatic classification algorithm was used to extract the information of urban and rural residential areas in the surrounding area of Qinghai lake watershed. The average recall rate, accuracy rate and F value were used to evaluate the classification results. Accuracy evaluation indexes of urban residential areas were more than 93%, and those of rural residential areas were more than 86%. The results show that this method has higher overall precision when extracting urban residential areas and rural residential areas, and has better scientific significance and application value in fine monitoring of human activities in large areas.
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Received: 17 September 2019
Published: 15 September 2020
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Corresponding Authors:
Yuan Qi
E-mail: lianxh@lzb.ac.cn;qiyuan@lzb.ac.cn
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