面向对象的煤矸石堆场SPOT-5影像识别

doi:10.3724/SP.J.1047.2015.00369

地球信息科学学报 ›› 2015, Vol. 17 ›› Issue (3): 369-377.doi: 10.3724/SP.J.1047.2015.00369

• • 上一篇

面向对象的煤矸石堆场SPOT-5影像识别

黄丹^1,²(), 刘庆生¹(), 刘高焕¹, 闫文博³

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 Dan^1,²(), LIU Qingsheng^1,^*(), LIU Gaohuan¹, YAN Wenbo³

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

摘要/Abstract

摘要：

煤矸石是一种在成煤过程中与煤层伴生的黑灰色固体废弃物,不仅会污染环境,而且会严重损害附近居民的身体健康,目前已经成为矿区生态环境的主要影响源之一。因此,实时、准确、快速地获取煤矸石堆场的位置、形状和面积等信息,对于环境监测与管理具有重要的意义。本文以内蒙古鄂尔多斯市东胜区为试验区,将试验区内的典型地物分为：植被、水体、阴影、裸地、建筑、道路、排土排矸场、露天煤矸石堆场、堆煤场及煤渣、采煤坑和其他共11类。本文采用SPOT-5高分辨率遥感影像,面向对象提取研究区内的煤矸石堆场信息,并进行识别精度评价,精度达到89.47%。将面向对象的分类结果与最大似然分类方法的分类结果进行比较,结果表明,面向对象的提取方法可更好地应用于煤矸石堆场信息的自动提取,大幅度提高精度和效率。

关键词: 遥感, SPOT-5, 面向对象, 多尺度分割, 规则集, 煤矸石堆场

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.

Key words: Remote sensing, SPOT-5, object-oriented, multi-resolution segmentation, rule set, coal gangue yards

黄丹, 刘庆生, 刘高焕, 闫文博. 面向对象的煤矸石堆场SPOT-5影像识别[J]. 地球信息科学学报, 2015, 17(3): 369-377.DOI:10.3724/SP.J.1047.2015.00369

HUANG Dan,LIU Qingsheng,LIU Gaohuan,YAN Wenbo. Coal Gangue Yards Information Extraction Using Object-oriented Method Based on SPOT-5 Remote Sensing Images[J]. Journal of Geo-information Science, 2015, 17(3): 369-377.DOI:10.3724/SP.J.1047.2015.00369

图/表 11

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参考文献 30

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典型地物	分割参数
典型地物	尺度参数	光谱因子	形状因子	紧致度	光滑度
阴影	40	0.8	0.2	0.5	0.5
植被	40	0.8	0.2	0.5	0.5
水体	160	0.8	0.2	0.6	0.4
建筑物	160	0.8	0.2	0.6	0.4
道路	160,80	0.8	0.2	0.6	0.4
露天煤矸石堆场	80	0.9	0.1	0.6	0.4
采煤坑	80	0.9	0.1	0.6	0.4
堆煤场	80	0.9	0.1	0.6	0.4
排土排矸场	400	0.9	0.1	0.6	0.4
裸地	400	0.9	0.1	0.6	0.4

影像层次	地物类别	分类特征	函数值
Level 1	裸地	Brightness	[119,126]
		Ratio-swir	[0.081,0.084]
		GLCM Mean（all dir.）	[4.3,7.2]
	排土排矸场（在类别裸地中提取）	Area	[32 000,35 000]
	排土排矸场（在类别裸地中提取）	Shape Index	[0.26,0.3]
Level 2	建筑	NDBI	[0.17,0.2]
	建筑	GLCM Contrast	[13,15]
	宽道路（在类别建筑中提取）	Length	[180,200]
	宽道路（在类别建筑中提取）	Length/Width	[7,7.4]
	水体	MNDWI	[-0.1,0]
Level 3	采煤坑	Brightness	[83,89]
		Area	[19 000,34 000]
		Distance to	2000
	堆煤场、煤渣（在类别采煤坑中提取）	Rectangular Fit	[0.43,0.52]
	露天煤矸石堆场	Distance to	2000
		Brightness	[101,119]
		Area	[20 000,27 900]
	窄道路	Brightness	[106,114]
	窄道路	Length/Width	[4.7,5.5]
Level 4	阴影	Brightness	[63,70]
Level 4	植被	SAVI	[0.1,0.12]

地物类型	采煤坑	道路	堆煤场	建筑	露天煤矸石堆场	裸地	排土排矸场	水体	阴影	植被
生产者精度(%)	80.00	87.50	81.58	88.04	79.07	95.16	88.46	96.43	90.03	89.80
用户精度(%)	100.00	90.84	84.93	90.44	89.47	84.29	90.79	83.08	89.56	86.94
总体精度(%)											88.78
Kappa											0.8649

地物类型	采煤坑	道路	堆煤场	建筑	露天煤矸石堆场	裸地	排土排矸场	水体	阴影	植被
生产者精度(%)	71.54	56.10	62.60	62.60	61.43	65.90	69.47	66.70	58.60	62.00
用户精度(%)	60.33	66.20	69.70	65.30	64.18	63.00	77.65	61.50	58.00	60.30
总体精度(%)											64.13
Kappa											0.5712

面向对象的煤矸石堆场SPOT-5影像识别

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

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