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### 面向对象的煤矸石堆场SPOT-5影像识别

1. 1. 中国科学院地理科学与资源研究所 资源与环境信息系统国家重点实验室,北京 100101
2. 中国科学院大学,北京 100049
3. 西藏自治区测绘局,拉萨 850000
• 收稿日期:2014-05-13 修回日期:2014-10-28 出版日期:2015-03-10 发布日期:2015-03-10
• 作者简介:

作者简介：黄 丹(1989-),女,江苏南通人,硕士生,研究方向为遥感与地理信息系统的应用研究。E-mail:huangdan21001@gmail.com

• 基金资助:
环保公益资助项目(201109043)

### Coal Gangue Yards Information Extraction Using Object-oriented Method Based on SPOT-5 Remote Sensing Images

HUANG Dan1,2(), LIU Qingsheng1,*(), LIU Gaohuan1, YAN Wenbo3

1. 1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
3. Surveying and mapping Bureau of Tibet Autonomous Region, Lhasa 100101, China
• Received:2014-05-13 Revised:2014-10-28 Online:2015-03-10 Published:2015-03-10
• Contact: LIU Qingsheng E-mail:huangdan21001@gmail.com;liuqs@lreis.ac.cn
• About author:

*The author: SHEN Jingwei, E-mail:jingweigis@163.com

Abstract:

Coal gangue is a kind of dark gray solid waste generated during mining process. Nowadays, coal gangue has become one of the biggest pollution sources to the ecological environment in mining areas. The accumulation of coal gangue not only occupies excessive land and causes serious environmental problem, but also damages the health of local people. Therefore, it is urgent to reduce the coal gangue yards in mining areas. In addition, extracting the location, shape and size information of coal gangue yards quickly and accurately is significant to environmental departments. The traditional methods of investigating coal gangue yards cost a lot of time and money. While remote sensing technologies can record the information of earth surface quickly and accurately, they have obvious superiorities in extracting coal gangue yards information. This paper takes the Dongsheng District, which locates in Ordos City of Inner Mongolia, as the study area, and utilizes SPOT-5 high resolution image as the data source. Then, this paper adopts the object-oriented method to extract coal gangue yards information from the study area. Multi-resolution segmentation and fuzzy classification algorithm are the most important steps in this method. Four appropriate segmentation scales are determined through comparisons of several tests, they are: 400, 160, 80 and 40. Next, we classify the segmented objects into different classes using the fuzzy classification algorithm that based on objects’ characteristics, such as spectrum, shape, texture and other features. The objects are further classified into eleven classes: bare area, buildings, roads, water, vegetation, shadows, dumps, coal gangue yards, coal yards, coal pits and others. The rule set used to extract different classes is built, which is aimed to provide a reference to relevant environmental departments to quickly and conveniently monitor the environment in coal mining area. In the end, we assess the accuracy of the classification results: the total accuracy is 88.78% and the user accuracy of coal gangue yards information is about 89.47%. Besides, a comparative extraction result is extracted using maximum likelihood method, whose total accuracy is 64.13% and the user accuracy of coal gangue yards information is only about 64.18%, which is much lower than the result extracted by the object-oriented method. Generally, due to the serious pollution caused by coal gangue yards, and considering the object-oriented classification method is seldom used to extract coal gangue yards information in China and abroad, this paper tries to extract coal gangue yards information using object-oriented classification method, and establish a rule set that can be applied to extract coal gangue yards information and other typical features. As we can see from the analyses, this paper has its significance in environmental protection. Relevant conclusions and analysis can be provided to the environmental protection departments as a useful reference to monitor and manage the pollution caused by coal gangue yards.