基于最优特征空间的震后损毁建筑物信息提取研究

Abstract

Abstract: This paper proposes an information extraction method which is based on traditional object-oriented method and the optimal feature space. In this method, images are segmented at an optimal scale by using ESP (estimate of scale parameter) tools. Then, distance matrix and minimum separation distance between various types of objects and ground are calculated, through the selected samples, so as to seek the optimal feature space. Finally, the post-earthquake images of damaged buildings are extracted according to the obtained optimal feature space. For the QuickBird image, the total precision of extraction is 83.1％ and the Kappa coefficient is 0.813. For the UAV(unmanned aerial vehicle) image, the total precision of extraction is 92.9％ and the Kappa coefficient is 0.940. Compared with the traditional object-oriented method, the precision and efficiency of the extraction method are greatly improved, which shows that this method has good popularization value in the information extraction of damaged buildings.

Key words: Optimal feature space, Damaged buildings, ESP

CLC Number:

TP79

CHEN Jin, CHEN Wen-kai, DOU Ai-xia, LI Wen, SUN Yan-ping. Information Extraction of Damaged Buildings after Earthquake Based on Optimal Feature Space[J]. EARTHQUAKE, 2019, 39(3): 71-83.

References

[1] Masashi Matsuoka, Fumio Yamazaki. Identification of Damaged areas Due to the 1995 Hyogoken-Nanbu Earthquake Using Satellite Optical Images. Proceedings of the 19th Asian Conference on Remote Sensing, Q9[C], 1998.
[2] Mitomi H, Yamazaki F, Matsuoka M. Development of automated extraction method for building damage area based on maximum likelihood classifier. Proceedings of 8th International Conference on Structual Safety and Reliability[C]. California: New Beech, 2001.
[3] 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], Int. J. Remote Sensing, 2004, 25(21): 4701-4714.
[4] Rathje E M, Crawford M, et al. Earthquake damage identification using multi-temporal high-resolution optical satellite imagery[C]. IEEE International 2005, (7): 5045-5048.
[5] Vu T T, Matsuoka M, Yamazaki F. Preliminary results in Development of an Object-based Image Analysis Method for Earthquake Damage Assessment. Proceeding of 3rd International workshop on Remote Sensing for Post-Disaster Response, September 2005, China, Japan.
[6] Janalipour M, Mohammadzadeh A. Building Damage Detection Using Object-Based Image Analysis and ANFIS From High-Resolution Image (Case Study: BAM Earthquake, Iran)[J]. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 2015, PP(99): 1-9.
[7] Yamazaki F, Liu W. Remote sensing technologies for post-earthquake damage assessment: A case study on the 2016 Kumamoto Earthquake[J]. Asia Conference on Earthquake Engineering, 2016.
[8] 张德成. 建筑物震害航空照片目视判读标志的初步研究[J]. 地震, 1993, 1: 26-30.
[9] 柳稼航, 杨建峰, 秦绪文. 城市房屋震害的区域分析法—与点特征判据算法的对比研究[J]. 自然灾害学报, 2004, 13(2): 149-156.
[10] 陈文凯. 面向震害评估的遥感应用技术研究[D]. 兰州: 中国地震局兰州地震研究所, 2007.
[11] 赵福军, 张磊. 面向对象的遥感震害信息提取方法—以汶川地震为例[J]. 地震, 2009, 29: 130-138.
[12] 王慧敏, 李艳. 面向对象的损毁建筑物提取[J]. 遥感信息, 2011, 26(5): 81-85.
[13] 李强, 张景发. 不同特征融合的震后损毁建筑物识别研究[J]. 地震研究, 2016, 39(3): 486-493.
[14] 刘莹, 陶超, 闫培, 等. 图割能量驱动的高分辨率遥感影像震害损毁建筑物检测[J]. 测绘学报, 2017, 46(7): 910-917.
[15] Dong L, Shan L. A comprehensive review of earthquake-induced building damage detection with remote sensing techniques[J]. ISPRS J Photogramm, 2013, 84: 85-99.
[16] Drǎgut, L, Tiede D, Levick S R. ESP: A tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data[J]. International Journal of Geographical Information Science, 2010, 24(6): 859-871.
[17] 陈铁桥, 陈杰, 梅小明, 等. 基于多层次场聚类的高分辨率遥感影像分割方法[J]. 地理与地理信息科学, 2013, 29(6): 10-13.
[18] 汪小钦, 王苗苗, 王绍强, 等. 基于可见光波段无人机遥感的植被信息提取[J]. 农业工程学报, 2015, 31(5): 152-159.
[19] Landis J R, Koch G G. An application of hierarchical kappa-type statistics in the assessment of majority agreement among multiple observers[J]. Biometrics, 1977, 33(2): 363-374.
[20] 赵妍, 张景发, 姚磊华. 基于面向对象的高分辨率遥感建筑物震害信息提取与评估[J]. 地震学报, 2016, 38(6): 942-951.

Information Extraction of Damaged Buildings after Earthquake Based on Optimal Feature Space

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