Road Change Detection Using Object-oriented Projective Interactive Partition

doi:10.6046/gtzyyg.2012.03.12

Abstract
References
Related Articles
Metrics

Download: PDF(1116 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract In this paper the authors present an object-oriented change detection method which uses projective interactive partition to detect the change of the urban road. The QuickBird images acquired in different years were used in the experiment, on which multi-scale segmentation and hierarchical classification were carried out for urban road extraction according to the spectrum, shape and texture features. Then the object layers were established in the same detection, and they were the projection layers taking over the classification results of asynchrony images. Interactive partition was also realized on these layers. Finally the change detection results were achieved after judging the consistency of land feature categories. The experiment results show that the method of object-oriented change detection using projective interactive partition can extract and detect the urban road effectively.

Keywords landscape pattern change analysis remote sensing image Longyan city

:	TP75
	P237

Issue Date: 20 August 2012

	Service

	E-mail this article
	E-mail Alert
	RSS
	Articles by authors

	CHEN Xue-ling
	CHEN Shao-jie
	DU Pei-jun
	XIA Jun-shi

Cite this article:

CHEN Xue-ling,CHEN Shao-jie,DU Pei-jun, et al. Road Change Detection Using Object-oriented Projective Interactive Partition[J]. REMOTE SENSING FOR LAND & RESOURCES, 2012, 24(3): 60-64.

URL:

https://www.gtzyyg.com/EN/10.6046/gtzyyg.2012.03.12 OR https://www.gtzyyg.com/EN/Y2012/V24/I3/60

[1] Klang D.Automatic Detection of Changes in Road Databases Using Satellite Imagery[J].GIS-between Visions and Applications,1998,32(4):293-298.
[2] 董明. 基于遥感影像的地图道路网数据的变化检测研究[D].武汉:武汉大学,2005. Dong M. The Change Detection of Road Network Map Data Based on Remote Sensing Image[D]. Wuhan: Wuhan University,2005 (in Chinese with English Abstract).
[3] Zhang Q P,Couloigner I. Automatic Road Change Detection and GIS Updating from High Spatial Remotely-sensed Imagery [J].Geo-spatial Information Science,2004,7(2):89-95.
[4] 唐炉亮,杨必胜,徐开明.基于线状图形相似性的道路数据变化检测[J].武汉大学学报:信息科学版,2008,33(4):367-370. Tang L L,Yang B S,Xu K M. The Road Data Change Detection Based on Linear Shape Similarity [J].Geomatics and Information Science of Wuhan University, 2008, 33(4):367-370 (in Chinese with English Abstract).
[5] 钟家强,王润生. 一种基于线特征的道路网变化检测算法[J].遥感学报,2007,11(1):27-32. Zhong J Q, Wang R S. A Road Network Change Detection Algorithm Based on Linear Feature[J].Journal of Remote Sensing, 2007, 11(1):27-32 (in Chinese with English Abstract).
[6] 沈澍. 遥感图像区域变化检测方法研究[D]. 安徽:中国科学技术大学,2008. Shen S. The Study on Region Change Detection of Remote Sensing Images[D]. Anhui: University of Science and Technology of China, 2008 (in Chinese with English Abstract).
[7] Mancini A, Frontoni E, Zingaretti P. Road Change Detection from Multi-spectral Aerial Data[C]//Proceedings of the 20th International Conference on Pattern Recognition.Washiagton,DC:IEEE,2010:448-451.
[8] Sui H G, Li D R, Gong J Y. Automatic Change Detection for Road Networks from Images Based on GIS[J].Geo-spatial Information Science,2003,6(4):44-50.
[9] Ziems M, Beyen J, Mueller S, et al. Multiple-model Based Update of Belgian Reference Road Data[C]//ISPRS Workshop on Geospatial Data Infrastructure:From Data Acquisition and Updating to Smarter Services.Guilin,China, 2011:73-79.
[10] 李晓峰,张树清,韩富伟,等.基于多重信息融合的高分辨率遥感影像道路信息提取[J].测绘学报,2008,37(2):178-184. Li X F, Zhang S Q, Han F W, et al. Road Extraction from High-resolution Remote Sensing Images Based on Multiple Information Fusion[J]. Acta Geodaetica et Cartographica Sinica, 2008, 37(2):178-184 (in Chinese with English Abstract).
[11] 韩闪闪,李海涛,顾海燕.面向对象的土地利用变化检测方法研究[J].遥感应用,2009(3):23-29. Han S S, Li H T, Gu H Y. The Study on Land Use Change Detection Based on Object-oriented Analysis [J]. Remote Sensing Application, 2009(3):23-29 (in Chinese with English Abstract).
[12] 孙家抦.遥感原理与应用[M].武汉:武汉大学出版社,2009. Sun J B. Principle and Application of Remote Sensing[M]. Wuhan: Wuhan University Press, 2009(in Chinese).
[13] 施海亮,周绍光,赵建泉.遥感影像中城市道路的提取[J].北京测绘,2006(2):29-32. Shi H L, Zhou S G, Zhao J Q. Road Extraction of Urban Area from Remote Sensing Images[J]. Beijing Surveying and Mapping, 2006(2):29-32 (in Chinese with English Abstract).
[14] 田新光,张继贤,张永红.利用QuickBird影像的阴影提取建筑物高度[J].测绘科学,2008,33(2):88-89. Tian X G, Zhang J X, Zhang Y H. Extraction of Heights of Buildings in City from Shadows in QuickBird Image[J]. Science of Surveying and Mapping, 2008,33(2):88-89 (in Chinese with English Abstract).
[15] 周亚男,沈占锋,骆剑承,等.阴影辅助下的面向对象城市建筑物提取[J].地理与地理信息科学,2010,26(3):37-40. Zhou Y N, Shen Z F, Luo J C, et al. Shadow-assisted Object- oriented Extraction of Urban Buildings[J]. Geography and Geo- information Science, 2010,26(3):37-40 (in Chinese with English Abstract).
[16] 唐伟,赵书河,王培法.面向对象的高空间分辨率遥感影像道路信息的提取[J].地球信息科学,2008,10(2):257-261. Tang W, Zhao S H, Wang P F.Object-oriented Road Information Extraction from High Resolution Imagery[J]. Geo-information Science, 2008,10(2):257-261 (in Chinese with English Abstract).
[17] Ni C, Ye Q, Li B F, et al. Road Extraction from High-resolution Remote Sensing Image Based on Phase Classification[J]. Remote Sensing and Spatial Information Sciences,2008,XXXⅦ(B3b):509-514.
[18] 王文杰,赵忠明,朱海青.面向对象特征融合的高分辨率遥感图像变化检测方法[J].计算机应用研究,2009,26(8):3149-3151. Wang W J, Zhao Z M, Zhu H Q. Object-oriented Change Detection Method Based on Features Fusion for High-resolution Remote Sensing Image[J]. Application Research of Computers, 2009,26(8):3149-3151 (in Chinese with English Abstract).
[19] 霍春雷,程健,周志鑫,等.基于尺度传播的多尺度变化检测新方法[C]//第十四届全国图象图形学学术会议论文集.2008:372-376. Huo C L, Cheng J, Zhou Z X, et al. Multiscale Change Detection Based on Scale Propagation[C]// The 14th National Conference on Image and Graphics.2008:372-376(in Chinese with English Abstract).
[20] 眭海刚.基于特征的道路网自动变化检测方法研究[D].武汉:武汉大学,2002. Sui H G. Automatic Change Detection for Road Networks Based on Features[D].Wuhan: Wuhan University, 2002 (in Chinese with English Abstract).

[1]	ZHANG Daming, ZHANG Xueyong, LI Lu, LIU Huayong. Remote sensing image segmentation based on Parzen window density estimation of super-pixels[J]. Remote Sensing for Natural Resources, 2022, 34(1): 53-60.
[2]	XUE Bai, WANG Yizhe, LIU Shuhan, YUE Mingyu, WANG Yiying, ZHAO Shihu. Change detection of high-resolution remote sensing images based on Siamese network[J]. Remote Sensing for Natural Resources, 2022, 34(1): 61-66.
[3]	SONG Renbo, ZHU Yuxin, GUO Renjie, ZHAO Pengfei, ZHAO Kexin, ZHU Jie, CHEN Ying. A method for 3D modeling of urban buildings based on multi-source data integration[J]. Remote Sensing for Natural Resources, 2022, 34(1): 93-105.
[4]	LI Yikun, YANG Yang, YANG Shuwen, WANG Zihao. A change vector analysis in posterior probability space combined with fuzzy C-means clustering and a Bayesian network[J]. Remote Sensing for Natural Resources, 2021, 33(4): 82-88.
[5]	LI Teya, SONG Yan, YU Xinli, ZHOU Yuanxiu. Monthly production estimation model for steel companies based on inversion of satellite thermal infrared temperature[J]. Remote Sensing for Natural Resources, 2021, 33(4): 121-129.
[6]	LIU Zhizhong, SONG Yingxu, YE Runqing. An analysis of rainstorm-induced landslides in northeast Chongqing on August 31, 2014 based on interpretation of remote sensing images[J]. Remote Sensing for Natural Resources, 2021, 33(4): 192-199.
[7]	WANG Meiya, XU Hanqiu. A comparative study on the changes in heat island effect in Chinese and foreign megacities[J]. Remote Sensing for Natural Resources, 2021, 33(4): 200-208.
[8]	ZHANG Chengye, XING Jianghe, LI Jun, SANG Xiao. Recognition of the spatial scopes of tailing ponds based on U-Net and GF-6 images[J]. Remote Sensing for Natural Resources, 2021, 33(4): 252-257.
[9]	WANG Yiuzhu, HUANG Liang, CHEN Pengdi, LI Wenguo, YU Xiaona. Change detection of remote sensing images based on the fusion of co-saliency difference images[J]. Remote Sensing for Natural Resources, 2021, 33(3): 89-96.
[10]	LIU Wanjun, GAO Jiankang, QU Haicheng, JIANG Wentao. Ship detection based on multi-scale feature enhancement of remote sensing images[J]. Remote Sensing for Natural Resources, 2021, 33(3): 97-106.
[11]	SANG Xiao, ZHANG Chengye, LI Jun, ZHU Shoujie, XING Jianghe, WANG Jinyang, WANG Xingjuan, LI Jiayao, YANG Ying. Application of intensity analysis theory in the land use change in Yijin Holo Banner under the background of coal mining[J]. Remote Sensing for Natural Resources, 2021, 33(3): 148-155.
[12]	LU Qi, QIN Jun, YAO Xuedong, WU Yanlan, ZHU Haochen. Buildings extraction of GF-2 remote sensing image based on multi-layer perception network[J]. Remote Sensing for Land & Resources, 2021, 33(2): 75-84.
[13]	HU Suliyang, LI Hui, GU Yansheng, HUANG Xianyu, ZHANG Zhiqi, WANG Yingchun. An analysis of land use changes and driving forces of Dajiuhu wetland in Shennongjia based on high resolution remote sensing images: Constraints from the multi-source and long-term remote sensing information[J]. Remote Sensing for Land & Resources, 2021, 33(1): 221-230.
[14]	LIU Zhao, ZHAO Tong, LIAO Feifan, LI Shuai, LI Haiyang. Research and comparative analysis on urban built-up area extraction methods from high-resolution remote sensing image based on semantic segmentation network[J]. Remote Sensing for Land & Resources, 2021, 33(1): 45-53.
[15]	WANG Xiaobing. Denoising algorithm based on the fusion of lifting wavelet thresholding and multidirectional edge detection of remote sensing image of mining area[J]. Remote Sensing for Land & Resources, 2020, 32(4): 46-52.

Viewed

Full text

Abstract

Cited

Shared

Discussed