The change detection method based on object-oriented characteristic mapping pattern analysis

doi:10.6046/gtzyyg.2013.03.09

Abstract
References
Related Articles
Metrics

Download: PDF(2588 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

This paper proposes a object-oriented change detection method based on the characteristic mapping pattern analysis. The method is improved from the information transfer model of remote sensing image interpretation. The image objects are acquired by the vector auxiliary data. The spectral and texture features are extracted, and an unsupervised clustering method is used to obtain the characteristic clusters of the objects. According to the priori information which exists in the auxiliary data, the mapping between the multi-temporal feature clusters is analyzed class by class. Then, the change object, whose mapping mode is inconsistent with other objects of the same class, can be identified. The experiments prove the feasibility and effectiveness of the proposed method, and the results show a new way for the object-oriented change detection.

Keywords Kumtagh sand dunes crescent dune chaines complex longitudinal sand dunes remote sensing

TP 75

Issue Date: 03 July 2013

	Service

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

	JIAO Yinxia
	MU Yuanrui
	ZHANG Wangsheng
	GUO Jie
	CHEN Yilin
	LI Xiaomu

Cite this article:

JIAO Yinxia,MU Yuanrui,ZHANG Wangsheng, et al. The change detection method based on object-oriented characteristic mapping pattern analysis[J]. REMOTE SENSING FOR LAND & RESOURCES, 2013, 25(3): 51-55.

URL:

https://www.gtzyyg.com/EN/10.6046/gtzyyg.2013.03.09 OR https://www.gtzyyg.com/EN/Y2013/V25/I3/51

[1] 苏伟,李京,陈云浩,等.基于多尺度影像分割的面向对象城市土地覆被分类研究——以马来西亚吉隆坡市城市中心区为例[J].遥感学报,2007,11(4):521-530. Su W,Li J,Chen Y H,et al.Object-oriented urban land-cover classification of multi-scale image segmentation method:A case study in Kuala Lumpur city center,Malaysia[J].Journal of Remote Sensing,2007,11(4)521-530.

[2] 赵宇鸾,林爱文.基于面向对象和多尺度影像分割技术的城市用地分类研究——以武汉市城市中心区为例[J].国土资源科技管理,2008,25(5):90-95. Zhao Y L,Lin A W.Urban land cover classification research based on object-oriented and multi-scale image segmentation techniques:A case study of downtown area of Wuhan city[J].Scientific and Technological Management of Land and Resources.2008,25(5):90-95.

[3] 白穆,刘慧平,乔瑜,等.高分辨率遥感图像分类方法在LUCC中的研究进展[J].国土资源遥感,2010,22(1):19-23. Bai M,Liu H P,Qiao Y,et al.New progress in the classification of high spatial resolution satellite images for LUCC[J].Remote Sensing for Land and Resources,2010,22(1):19-23.

[4] Zomeni M,Tzanopoulos J T,Pantis J D,et al.Historical analysis of landscape change using remote sensing techniques:An explanatory tool for agricultural transformation in Greek rural areas[J].Landscape Urban Plan 2008,86(1):38-46.

[5] Conchedda G,Durieux L,Mayaux P.An object-based method for mapping and change analysis in mangrove ecosystems[J].Photogrammetry and Remote Sensing,2008,63(5):578-589.

[6] Jacquin A,Misakova L,Gay M.A hybrid object-based classification approach for mapping urban sprawl in periurban environment[J].Landscape and Urban Planning,2008,84(2):152-165.

[7] Hay G J,Castilla G.Geographic object-based image analysis(GEOBIA):A new name for a new discipline［G］//Blaschke T,Lang S,Hay G J.Object-Based Image Analysis.Berlin,New York,Heidelberg:Springer,2008:75-89.

[8] Walter V.Automatic change detection in GIS databases based on classification of multispectral data［C］//International Archives of Photogrammetry and Remote Sensing,2000,XXXIII(B4)1138-1145.

[9] Walter V.Object-based classification of remote sensing data for change detection[J].ISPRS Journal of Photogrammetry and Remote Sensing,2004,58(3/4):225-238.

[10] Blaschke T.Object based image analysis for remote sensing[J].ISPRS Journal of Photogrammetry and Remote Sensing,2010,65(1):2-16.

[11] 舒宁.关于遥感影像处理分析的理论与方法之若干问题[J],武汉大学学报:信息科学版,2007,32(11):1007-1010. Shu N.Aspects of the theories and mehodologies for remote sensed image processing and analysis[J].Geomatics and Information Science of Wuhan University,2007,32(11):1007-1010.

[12] Datcu M,Seidel K,Pelizarri A,et al.Image information mining and remote sensing data interpretation［C］//Proceedings of Geoscience and Remote Sensing Symposium,IGARSS 2000.2000 International:3057-3059.

[13] 《全国土地分类》(过渡期间适用)［EB/OL］.(2006-4-14)［2012-8-21］.http://www.diji.com.cn/xl-8-12-5.html. "National Land Classification"(apply during the transitional period)［EB/OL］.(2006-4-14)［2012-8-21］.http://www.diji.com.cn/xl-8-12-5.html.

[1]	LI Weiguang, HOU Meiting. A review of reconstruction methods for remote-sensing-based time series data of vegetation and some examples[J]. Remote Sensing for Natural Resources, 2022, 34(1): 1-9.
[2]	DING Bo, LI Wei, HU Ke. Inversion of total suspended matter concentration in Maowei Sea and its estuary, Southwest China using contemporaneous optical data and GF SAR data[J]. Remote Sensing for Natural Resources, 2022, 34(1): 10-17.
[3]	GAO Qi, WANG Yuzhen, FENG Chunhui, MA Ziqiang, LIU Weiyang, PENG Jie, JI Yanzhen. Remote sensing inversion of desert soil moisture based on improved spectral indices[J]. Remote Sensing for Natural Resources, 2022, 34(1): 142-150.
[4]	ZHANG Qinrui, ZHAO Liangjun, LIN Guojun, WAN Honglin. Ecological environment assessment of three-river confluence in Yibin City using improved remote sensing ecological index[J]. Remote Sensing for Natural Resources, 2022, 34(1): 230-237.
[5]	HE Peng, TONG Liqiang, GUO Zhaocheng, TU Jienan, WANG Genhou. A study on hidden risks of glacial lake outburst floods based on relief amplitude: A case study of eastern Shishapangma[J]. Remote Sensing for Natural Resources, 2022, 34(1): 257-264.
[6]	LIU Wen, WANG Meng, SONG Ban, YU Tianbin, HUANG Xichao, JIANG Yu, SUN Yujiang. Surveys and chain structure study of potential hazards of ice avalanches based on optical remote sensing technology: A case study of southeast Tibet[J]. Remote Sensing for Natural Resources, 2022, 34(1): 265-276.
[7]	WANG Qian, REN Guangli. Application of hyperspectral remote sensing data-based anomaly extraction in copper-gold prospecting in the Solake area in the Altyn metallogenic belt, Xinjiang[J]. Remote Sensing for Natural Resources, 2022, 34(1): 277-285.
[8]	LYU Pin, XIONG Liyuan, XU Zhengqiang, ZHOU Xuecheng. FME-based method for attribute consistency checking of vector data of mines obtained from remote sensing monitoring[J]. Remote Sensing for Natural Resources, 2022, 34(1): 293-298.
[9]	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.
[10]	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.
[11]	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.
[12]	YU Xinli, SONG Yan, YANG Miao, HUANG Lei, ZHANG Yanjie. Multi-model and multi-scale scene recognition of shipbuilding enterprises based on convolutional neural network with spatial constraints[J]. Remote Sensing for Natural Resources, 2021, 33(4): 72-81.
[13]	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.
[14]	AI Lu, SUN Shuyi, LI Shuguang, MA Hongzhang. Research progress on the cooperative inversion of soil moisture using optical and SAR remote sensing[J]. Remote Sensing for Natural Resources, 2021, 33(4): 10-18.
[15]	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.

Viewed

Full text

Abstract

Cited

Shared

Discussed