Combining the matter element model with the associated function of probability transformation for multi-source remote sensing data classification in mountainous regions

CORC > 成都山地灾害与环境研究所 > 中国科学院水利部成都山地灾害与环境研究所 > 数字山地与遥感应用中心

	Combining the matter element model with the associated function of probability transformation for multi-source remote sensing data classification in mountainous regions
	Li, Ainong1,3 ; Jiang, Jingang 2; Bian, Jinhu1 ; Deng, Wei1
刊名	ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
	2012
卷号	67 期号:1 页码:80-92
关键词	Remote sensing Classification Matter element model Associated function Mountainous region
ISSN号	0924-2716
通讯作者	Li Ainong(李爱农)
合作状况	国际
英文摘要	That the multi-source remote sensing information integrates knowledge-based geospatial constraints to develop efficient and practical Land cover classification algorithm has become one of the most important developing directions in the field of remote sensing ground object classification. Remote sensing classification is a strictly incompatible problem, but the spectra distribution of remote sensing data has compatible attributes especially in mountainous regions, and such contradiction is one of the main reasons leading to uncertainties in remote sensing classification. In this paper, the remote sensing spectra feature compatible information is transformed into the probability of the association degree firstly, and then the matter-element theory is introduced to establish models to achieve the integrated classification of multi-source data to fuse knowledge-based geographical constrained condition probability. Taking the grass-land-wetland fragile ecosystem in Ruoergai plateau of China as a case study, this paper selected the multi-source data including images of Landsat TM and CBERS, ASTER-GDEM and MODIS-NDVI to construct a comprehensive classifier, in which the relationship between topography and land cover, and the prior knowledge on vegetation growth difference were taken as constraints to support the decision-making. The classification accuracy was evaluated by a field investigation and existing land cover map. The test result shows that, the overall accuracy (89.89%) and Kappa coefficient (0.8870) are better than those derived by the Maximum Likelihood method. It indicates that the proposed classification method is not subject to the dimensionality and form of data sources, and it can integrate the data source information to improve the classification accuracy, so that it is very useful to apply multi-source data and prior knowledge to land cover classification in mountainous regions. (C) 2011 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier B.V. All rights reserved.
学科主题	摄影测量与遥感技术
WOS标题词	Science & Technology ; Physical Sciences ; Technology
类目[WOS]	Geography, Physical ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
研究领域[WOS]	Physical Geography ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
关键词[WOS]	LAND-COVER CLASSIFICATION ; SUPPORT VECTOR MACHINES ; SENSED DATA ; IMAGE CLASSIFICATION ; ACCURACY ASSESSMENT ; DESIGN ; CHINA ; RIVER ; OPTIMIZATION ; CLASSIFIERS
收录类别	SCI
语种	英语
WOS记录号	WOS:000300749900009
公开日期	2012-12-13
内容类型	期刊论文
源URL	[http://192.168.143.20:8080/handle/131551/4475]
专题	成都山地灾害与环境研究所_数字山地与遥感应用中心成都山地灾害与环境研究所_山区发展研究中心
作者单位	1.Chinese Acad Sci, Inst Mt Hazards & Environm, Chengdu 610041, Peoples R China 2.Hangzhou Normal Univ, Acad Remote Sensing & Earth Sci, Hangzhou 310036, Zhejiang, Peoples R China 3.Univ Maryland, Dept Geog, College Pk, MD 20741 USA
推荐引用方式 GB/T 7714	Li, Ainong,Jiang, Jingang,Bian, Jinhu,et al. Combining the matter element model with the associated function of probability transformation for multi-source remote sensing data classification in mountainous regions[J]. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,2012,67(1):80-92.
APA	Li, Ainong,Jiang, Jingang,Bian, Jinhu,&Deng, Wei.(2012).Combining the matter element model with the associated function of probability transformation for multi-source remote sensing data classification in mountainous regions.ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,67(1),80-92.
MLA	Li, Ainong,et al."Combining the matter element model with the associated function of probability transformation for multi-source remote sensing data classification in mountainous regions".ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 67.1(2012):80-92.