基于高分辨率遥感目标特征库的地震灾情快速评估方法

doi:10.3724/SP.J.1047.2016.00699

摘要/Abstract

摘要：

高分辨率遥感影像已逐步成为地震灾害快速评估的主要数据源之一,但现有的遥感地震灾害信息提取方法存在对研究人员目视解译经验依赖性强和利用高分辨率影像提取结果精度不高的问题。因此,本文提出了一种基于目标特征库的高分辨率遥感灾害信息快速提取方法,用于提升遥感影像灾害信息提取的效率和自动化程度,并对基于目标特征库进行地震灾情快速评估的几项关键技术（目标特征库构建、样本匹配方法和自动分类方法）进行了阐述,最后,以云南鲁甸地震龙头山镇地区为研究区,基于高分辨率遥感影像在目标特征库支持下开展了地震灾情快速评估实验。通过与灾后调查数据的对比分析发现,基于高分辨率遥感灾害目标特征库的地震灾情快速评估结果在精度上可以满足灾情快速评估的业务需要,同时还具有更好的时效性。

关键词: 地震灾害评估, 目标特征库, 自动分类, 减灾应用, 高分辨率遥感

Abstract:

High-resolution remote sensing image has become a major source of information for the rapid assessment of earthquake disaster, and it also brings new challenges to the study and application of seismic disaster information extraction methods that are based on the remote sensing. The existing methods of seismic disaster information extraction that are based on the remote sensing technology have some defects, such as the high dependence on the visual interpretation experience of researchers and the low accuracy of extraction results produced from the high-resolution images. This paper provides a rapid extraction method of the high-resolution remote sensing seismic disaster information with the integration of target feature library. Via building the high-resolution remote sensing disaster target feature library, this method is capable to provide services for the accumulation and application of disaster features based on the high-resolution images, thus to meet the purpose of reducing the dependence on the experience of researchers and improving the automation level and efficiency of disaster information extraction from remote sensing images. Regarding to the method framework description, this paper introduces several key technologies in the progress of earthquake disaster rapid assessment, which includes building the target feature library and conducting the method of sample matching and automatic classification. This paper takes the earthquake prone region of Ludian, Yunnan as an example. The study area is in the central area of Longtoushan town, and the earthquake disaster rapid assessment experiment is supported by the high-resolution remote sensing target feature library. Comparing our experimental results with the field survey results, it shows that the accuracy of the experimental results can meet the service requirements of the rapid assessment. It also shows that the rapid extraction of seismic disaster based on high-resolution remote sensing disaster target feature library can effectively reduce the labor workload and strongly improve the automation level of services. Generally, this method has a positive significance to the disaster emergency response.

Key words: earthquake disaster assessment, target feature library, automatic classification, disaster mitigation application, high-resolution remote sensing

董文, 沈占锋, 程希萌. 基于高分辨率遥感目标特征库的地震灾情快速评估方法[J]. 地球信息科学学报, 2016, 18(5): 699-707.DOI:10.3724/SP.J.1047.2016.00699

DONG Wen,SHEN Zhanfeng,CHENG Ximeng. The Rapid Assessment Method of Earthquake Disaster Based on High-Resolution Remote Sensing Target Feature Library[J]. Journal of Geo-information Science, 2016, 18(5): 699-707.DOI:10.3724/SP.J.1047.2016.00699

图/表 11

图1

图2

图3

图4

图5

图6

图7

图8

图9

表1

表2

参考文献 18

[1]	民政部.民政部国家减灾办发布2013年全国自然灾害基本情况[EB/OL].,2014-01-04.
	[Ministry of Civil Affairs. National Disaster Reduction Office of Ministry of Civil Affairs has published the 2013 national natural disaster general information[EB/OL]. , 2014-01-04. ]
[2]	张景发,谢礼立,陶夏新.建筑物震害遥感图像的变化检测与震害评估[J].自然灾害学报,2002,11(2):59-64. doi: 10.3969/j.issn.1004-4574.2002.02.010
	[ Zhang J F, Xie L L, Tao X X.Change detection of remote sensing image for earthquake-damaged buildings and its application in seismic disaster assessment[J]. Journal of Natural Disasters, 2002,11(2):59-64. ] doi: 10.3969/j.issn.1004-4574.2002.02.010
[3]	苏凤环,刘洪江,韩用顺.汶川地震山地灾害遥感快速提取及其分布特点分析[J].遥感学报,2008,12(6):956-963.
	[ Su F H, Liu H J, Han Y S.The extraction of mountain hazard induced by Wenchuan earthquake and analysis of its distributing characteristic[J]. Journal of Remote Sensing, 2008,12(6):956-963. ]
[4]	Huyck C K, Adams B J, Cho S, et al.Towards rapid citywide damage mapping using neighborhood edge dissimilarities in very high-resolution optical satellite imagery-application to the 2003 Bam, Iran, Earthquake[J]. Earthquake Spectra, 2005,21(S1):S255-S266.
[5]	Turker M, San B T.Detection of collapsed buildings caused by the 1999 Izmit, Turkey earthquake through digital analysis of post-event aerial photographs[J]. International Journal of Remote Sensing, 2004,25(21):4701-4714. doi: 10.1080/01431160410001709976
[6]	黎小东. 面向对象的高空间分辨率遥感影像城市建筑物震害信息提取——以汶川县城为例[D].成都:成都理工大学,2009.
	[ Li X D.Information extraction of damaged town buildings caused by earthquake with the object-oriented method from high special resolution image: a case study in Wenchuan earthquake[D]. Chengdu: Chengdu University of Technology, 2009. ]
[7]	王岩,王晓青,窦爱霞.面向对象遥感分类方法在汶川地震震害提取中的应用[J].地震,2009,29(3):54-60. doi: 10.3969/j.issn.1000-3274.2009.03.007
	[ Wang Y, Wang X Q, Dou A X.Building damage detection of the 2008 Wenchuan, China earthquake based on object-oriented classification method[J]. Earthquake, 2009,29(3):54-60. ] doi: 10.3969/j.issn.1000-3274.2009.03.007
[8]	Kouchi K, Yamazaki F.Damage detection based on object-based segmentation and classification from high-resolution satellite images for the 2003 Boumerdes, Algeria earthquake[C]. Proceedings of the 26th Asian Conference on Remote Sensing, 2005:1-6.
[9]	Luscier J D, Thompson W L, Wilson J M, et al.Using digital photographs and object-based image analysis to estimate percent ground cover in vegetation plots[J]. Frontiers in Ecology and the Environment, 2006,4(8):408-413.
[10]	Vu T T, Matsuoka M, Yamazaki F.Preliminary results in development of an object-based image analysis method for earthquake damage assessment[C]. Proceeding of 3rd International Workshop on Remote Sensing for Post-Disaster Response, Chiba, Japan, 2005.
[11]	Liu S, Thonnat M, Berthod M.Automatic classification of planktonic foraminifera by a knowledge-based system[C]. Proceedings of the IEEE Tenth Conference on Artificial Intelligence for Applications, 1994:358-364.
[12]	Durand N, Derivaux S, Forestier G, et al.Ontology-based object recognition for remote sensing image interpretation[C]. 19th IEEE International Conference on Tools with Artificial Intelligence, 2007,1:472-479.
[13]	高伟. 基于特征知识库的遥感信息提取技术研究[D].武汉:中国地质大学,2010.
	[ Gao W.The study of information extraction technology for remote sensing based on feature knowledge[D]. Wuhan: China University of Geosciences, 2010. ]
[14]	Blaschke T, Hay G J, Kelly M, et al.Geographic object-based image analysis-towards a new paradigm[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2014,87:180-191.
[15]	Wu X, Kumar V, Quinlan J R, et al.Top 10 algorithms in data mining[J]. Knowledge and Information Systems, 2008,14(1):1-37. doi: 10.1007/s10115-007-0114-2
[16]	Quinlan J R.Bagging, boosting, and C4.5[C]. Proceedings of the Thirteenth National Conference on Artificial Intelligence and Eighth Innovative Applications of Artificial Intelligence Conference, 1996,1:725-730.
[17]	中国地震局办公室.中国地震局发布云南鲁甸6.5级地震烈度图[EB/OL], , 2014-08-07.
	[China Earthquake Administration Office. China Earthquake Administration has published the earthquake intensity map of Ludian, Yunnan 6.5 earthquake[EB/OL]. , 2014-08-07. ]
[18]	民政部国家减灾中心.云南鲁甸6.5级地震灾害遥感监测评估技术报告[R].北京:2014-08-29.
	[ National Disaster Reduction Commission, Ministry of Civil Affairs. Remote sensing monitoring evaluation technical report of Ludian, Yunnan, 6.5 earthquake[R].Beijing:2014-08-29. ]

	倒损率/（%）	参考烈度/度
街区1	70.24	8.94
街区2	60.89	8.47
街区3	72.50	9.06
街区4	64.10	8.63
街区5	20.61	6.00
街区6	55.96	8.21

	完全倒塌/m²	严重损毁/m²	一般损毁/m²	倒损总数/万m²	建筑总面积/万m²
砖木	98 966	75 787	82 241
砖混	79 105	150 356	205 916
钢混	0	27 501	33 469
其他	202 348	195 336	268 263
比例/(%)	23.09	27.25	35.80
合计				141.93	164.77