An Urban Open Space Extraction Method: Combining Spectral and Geometric Characteristics  

作　　者：ZHUGuobin DanG.Blumberg 

机构地区：[1]TheResearchCenterofSpatialInformationandDigitalEngineering,WuhanUniversity,129LuoyuRoad,Wuhan430079,China. [2]不详

出　　处：《Geo-Spatial Information Science》2004年第4期249-254,共6页地球空间信息科学学报（英文）

基　　金：FundedbytheScientificResearchFoundationfortheReturnedOverseasChineseScholars.

摘　　要：This paper introduces an advanced method based on remote sensing and Geographic Information System for urban open space extraction combining spectral and geometric characteristics. From both semantic and remote sensing perspectives, a hybrid hierarchy structure and class organization of open space are issues and mapped from one to another. Based on per-pixel and segmentation mechanism separately, two classification approaches are performed. Owing to prior of spatial aggregation and spectral contribution, the segmentation-based classification exhibits its superiority over a pixel-based classification. Finally a GIS-based post procedure is hired to eliminate some unsuitable open space components in both spatial and numerical constraints on the one hand, and separate open space some fabrics from fused remote sensing classes by defining their Shape Index on the other hand. The case study of Beer Sheva based on ASTER data proves this method is a feasible way for open space extraction.This paper introduces an advanced method based on remote sensing andGeographic Information System for urban open space extraction combining spectral and geometriccharacteristics. From both semantic and remote sensing perspectives, a hybrid hierarchy structureand class organization of open space are issues and mapped from one to another. Based on per-pixeland segmentation mechanism separately, two classification approaches are performed. Owing to priorof spatial aggregation and spectral contribution, the segmentation-based classification exhibits itssuperiority over a pixel-based classification. Finally a GIS-based post procedure is hired toeliminate some unsuitable open space components in both spatial and numerical constraints on the onehand, and separate open space some fabrics from fused remote sensing classes by defining theirShape Index on the other hand. The case study of Beer Sheva based on ASTER data proves this methodis a feasible way for open space extraction.

关 键 词：urban open space remote sensing geographic information systems (GIS) SEGMENTATION classification (ASTER 

分 类 号：P208[天文地球—地图制图学与地理信息工程] P237[天文地球—测绘科学与技术]