基于面向对象技术的土地利用/覆被分类研究
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摘要
随着IKOONOS、QuickBird的普及以及WorldView-1的出现,高空间分辨率影像成为研究土地利用、覆被变化的最主要数据源。高空间分辨率具有海量的空间信息,清晰地纹理信息,传统的分类方法不能有效地提取这些里面的空间信息。基于象元的传统分类方法在分类中会出现胡椒盐现象,出现无效图斑,不能保证分类精度。人们将影像分割技术应用到土地利用覆被分类、地物信息提取之中发展成为今天的面向对象分类方法。面向对象分类以对像为基本处理单位,通过尺度、形状/颜色、紧致度/光滑度等参数界定对象,并在分割的基础上进行分类。
面向对象的专业软件Definiens提供了很多种分割算法,包括四叉树分割、棋盘分割等,其中最普及的是多尺度分割。但是,在多尺度分割里的尺度参数选择一直困扰着学者们,有学者进行了这方面的探索,建立了最大面积法等准则,但是其太复杂,因此更多的研究者更乐意通过目视选择自己认为的最优尺度。
本文作者通过建立同质性指数和异质性指数,对实验区选择了四个层次的尺度,并建立了包括道路、河流、有林地、旱地、水浇地、房屋、灾毁地、阴影、丛树、荒草地、临时篷房在内的土地利用覆被系统,在分割的基础上,选择最邻近分类法及模糊分类法进行特征的选择,最后得到Kappa系数0.79的结果,分析原因在于植被、河流因为较好的光谱或几何特征容易被划分,而房屋因为光谱紊乱而没有得到很好的分类。
With the popularity of IKNONOS、QuickBird and the appearance of WorldView-1,High-resolution Image has been the main data source in the research of land use/cover. High-resolution image has vast information of space and clear texture. Traditional method of classification can’t extract the useful space information. the classification based on pixel would come to the“pepper-salt effect”, usefulness patch, which is failed in the accuracy assessment. People developed object-oriented classification, which is applied in the land use/cover classification or information extract, from the image segmentation.
Object-oriented classification worked base on the object, and define the object by scale, shape/color, compactness/smoothness.
Definiens, which is professional software, have some segmentation algorithm, such as quadtree segmentation or chessboard, and the multi-scale is the most popularity. The choosing of the best scale is bother the researchers, many people try to find a rule, such as the most area, as these rule is too complicated, most of the researcher choose the scale by eyes.
This paper create the index of ,and choose a set of scale for a hierarchical system of land use and cover, which is including river, road , forest, land ,irrigated land, house, damaged land ,shadow, tree cluster and so on. With the base of segmentation, classified the luc according to the nearest neighbor method and fuzzy classification.
With the result of Kappa coefficient is 0.79, maybe, it is classified easily to segmented the vegetation and river, which have a good feature of spectrum or geometry, at the same time, house was wrongly classified with disorder spectrum.
面向对象的专业软件Definiens提供了很多种分割算法,包括四叉树分割、棋盘分割等,其中最普及的是多尺度分割。但是,在多尺度分割里的尺度参数选择一直困扰着学者们,有学者进行了这方面的探索,建立了最大面积法等准则,但是其太复杂,因此更多的研究者更乐意通过目视选择自己认为的最优尺度。
本文作者通过建立同质性指数和异质性指数,对实验区选择了四个层次的尺度,并建立了包括道路、河流、有林地、旱地、水浇地、房屋、灾毁地、阴影、丛树、荒草地、临时篷房在内的土地利用覆被系统,在分割的基础上,选择最邻近分类法及模糊分类法进行特征的选择,最后得到Kappa系数0.79的结果,分析原因在于植被、河流因为较好的光谱或几何特征容易被划分,而房屋因为光谱紊乱而没有得到很好的分类。
With the popularity of IKNONOS、QuickBird and the appearance of WorldView-1,High-resolution Image has been the main data source in the research of land use/cover. High-resolution image has vast information of space and clear texture. Traditional method of classification can’t extract the useful space information. the classification based on pixel would come to the“pepper-salt effect”, usefulness patch, which is failed in the accuracy assessment. People developed object-oriented classification, which is applied in the land use/cover classification or information extract, from the image segmentation.
Object-oriented classification worked base on the object, and define the object by scale, shape/color, compactness/smoothness.
Definiens, which is professional software, have some segmentation algorithm, such as quadtree segmentation or chessboard, and the multi-scale is the most popularity. The choosing of the best scale is bother the researchers, many people try to find a rule, such as the most area, as these rule is too complicated, most of the researcher choose the scale by eyes.
This paper create the index of ,and choose a set of scale for a hierarchical system of land use and cover, which is including river, road , forest, land ,irrigated land, house, damaged land ,shadow, tree cluster and so on. With the base of segmentation, classified the luc according to the nearest neighbor method and fuzzy classification.
With the result of Kappa coefficient is 0.79, maybe, it is classified easily to segmented the vegetation and river, which have a good feature of spectrum or geometry, at the same time, house was wrongly classified with disorder spectrum.
引文
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[2] Aplin P,AtKinson P, Curran P. Per-field classification of Land Use Using the Forthcoming Very Fine Resolution Satellite Sensors: Problems and Potential Solutions [A]. In: Advances in Remote Sensing and Gis Analysis [C]. Chichester: Wiley and Son,1999: 219-239.
[3] Arigialis, D. P., and hlaz-low, C. A. Computation Image Interpretation Models: An Overview and a Perspective [J].Photogrammetric Engineering and Remote Sensing, 1990, 56(6): 871-886.
[4] Baatz M, Schape A. Object-Oriented and multi-scale image analysis in semantic networks[A].In:Proc of the 2nd International Symposium on Operationalization of Remote Sensing[C].August 16-20th 1999.Enschede ITC.
[5] Baudeouin Desclee, Patrick Bogaert, Pierre Defourny. Forest change detection by statistical object-based method. Remote Sensing of Environment, 2006 (10): 1-11. [ 6] Bauer,T., and Steinnocher,K. Per-parcel land use classification in urban areas applying a rule-based technique [J], Geo BIT/GIS, 2001, 6:12-27.
[7] Gao Yan, J . -F. MAS et al. Comparision of pixel-based and object-oriented image classification approaches-a case study in a coal fire area, Wuda, Inner Mongolia, China [J].International Journal of Remote Sensing, 2006, 27(18-20): 4039-4055.
[8] Hellwich O and Wieclemann C. Object Extration from Hith-Resolution Multi-sensor lmage Data [C]. ln: 3rd International Conference on Fusion of Earth Data, Sophia Antipolis, France,. 2000, 1:26-28, 105-115.
[9] Hofmann,P. Detecting buildings and roads from IKONOS data using additional elevation information, GeoBIT/GIS,2001b,(6):28-33.
[10] Hofmann,P. Detection informal settlements from IKONOS image data using methodsof object oriented image analysis-an example form Cape Town(South Africa)[J].Remote sensing of Urban Areas/Fernerk undung in urbanen Raumen.Regensburg,ed. By JURGENS, C., 2001a : 41-42.
[11] Huang Huiping, Wu Bingfang, Fan Jinlong. Analysis to the Relationship of Classification Accuracy Segmentation Scale Imabe Resolution [C]. IEEE Trans, IGARSS, 2003, 6: 3671-3673.
[12] Kasper Johansen, Nicholas C. Coops, et al. Application of high spatial resolution satellite imagery for riparian and forest ecosystem classification [J]. Remote Sensing of Environment, 2007(110): 29-44.
[13] Ketting, R. L., and Landgrebe, D.A. Computer classification of Remotely Sensed Multispetral image Data by Extraction and Classification of Homogeneous Object[J].IEEE Transactions on Geoscience Electronics, 1976,14(1):19-26.
[14] Lobo, A, Chic O and Casterad A. Classification of Mediterranean crops with multisensor data: per-pixel versus per-object statistics and image segmentation [J].International Journal of Remote Sensing, 1996, 17: 2358-2400.
[15] Matsu yama, T. Knowledge-Based Aerial Image Understanding Systems and Expert Systems for Image Processing [J]. IEEE Transactions on Geoscience and Remote Sensing, 1987, 25(1):305-316.
[16] Qian Yu, Peng Gong et al. Object-based Detailed Vegetation Classification with Airborne High Spatial Resolution Remote Sensing Imagery [J]. Photogrammetric Engineering&Remote Sensing, 2006, 72(7): 799-811.
[17] Renaud Mathieu, Jagannath Aryal. Object-oriented classification and IKONOS multispectral imagery for mapping vegetation communities in urban areas. The 17th Annual Colloquium of the Spatial Information Research Centre, 2005.
[18] Sande,C.,Van D. River Flood Damage Assessment Using IKONOS Imagery[A]. Natural Hazards Project-Floods, Space Application Institute, Joint Research Center, European Commission,1 spra.2001
[19] Ton, J., Sticklen, J., and Jain, A. K. Knorvledged-Based Segmentation of Landsat images [J]. IEEE Transactions on Geoscience and Remote Sensing, 1991, 29(2): 223-231.
[20] Wang Quan, Tenhunen John D. Vegetation mapping with multitemporal NDVI in NorthEastern China Transect [J]. International Journal of Applied Observation and Geoinformation, 2004(6): 17-31.
[21] Willhauck, G. Benz, U. C., and Siegert, F. Semiautomatic classification procedures for fire monitoring using multitemporal SAR images and NOAA-AVHRR hotspot data [C].Proceedings of the 4th European Conference on Synthetic Aperture Radar, Cologne, Germany, 2002,4-6.
[22] Zhang X. Y, Feng X. Z. Detecting urban vegetation using object-oriented method from IKONOS imagery. IGARS, 2005.
[23] Andrea S.Laliberte and Albert Rango et al. Object-oriented image analysis for mapping shrub encroachment from 1937 to 2003 in southern New Mexico[J].Remote Sensing of Environment. 2004, ( 93):198-210.
[24] Curtis Andrew Collins. Comparing integrated LiDAR and multispectral data with field measurements in hardwood stands [D]. Beijing: Beijing Forestry University Library, 2003.
[25] Damm,A.Phostert and S.SC Hiefer(2005): Investigaring urban railway corridors with Geometric high resolution satellite data[R].In: Proceedings of the IS PRSW VII/1"Human Settlements and Impact Analysis" 3rd International Symposium Remote Sensing and Data Fusion Over Urban Areas RBAN 2005) and 5th International Symposium Remote Sensing of Urban Areas (URS 2005).16, 2005.Tempe,A Z,USA: Marchl4-16.2005.
[26] Lemp. D and U. Weidener (2005): Improvements of roof surface classification using hyperspectral and laser scanning data[R]. ln: Proceedings of the 1SPRSW G VII/1 "Human Settlements and Impact Analysis" 3rd International Symposium Remote Sensing and Data Fusion Over Urban Areas (URBAN 2005) and 5th International Symposium Remote Sensing of Urban Areas(LJRS2005). Tempe, AZ, USA: March14-16, 2005.
[27] Sunil Reddy. Comparing special-object based approaches for extracting and classifying transportation features using high resolution multi-spectralsatellite imagery. Beijing: Beijing Forest ry University Library,2004.
[28] Van Aardt etc. An object-oriented approach to forest volume and aboveground biomass modeling using small-footprint lidar data for segmentation, estimation, and classification [D].Beijing: Beijing Forestry University Library,2004.
[29]曹宝,秦其明,马海建,等.面向对象方法在SPOT5遥感图像分类中的应用——以北京市海淀区为例[J].地理与地理信息科学,2006,(2).
[30]曹宝,秦其明,张自力,等.基于特征增强技术的面向对象分类方法[J].水土保持研究,2008,(1).
[31]曹雪,柯长青.基于对象级的高分辨率遥感影像分类研究[J].遥感信息,2006,(5).
[32]陈旭,许佐荣,余世孝.基于像元分类与基于对象分类的森林景观分析比较研究[J].福建林业科技,2008,(3).
[33]陈云浩,冯通,史培军,等.基于面向对象和规则的遥感影像分类研究[J].武汉大学学报(信息科学版),2006,(4).
[34]崔林丽,唐娉,赵忠明,郑柯,等.一种基于对象和多种特征整合的分类识别方法研究[J].遥感学报,2006,(1). [35 ]丁晓英.eCognition在土地利用项目中的应用[J].测绘与空间地理信息, 2005(6):116-117,120.
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