中国图象图形学报A
中國圖象圖形學報A
중국도상도형학보A
JOURNAL OF IMAGE AND GRAPHICS
2010年
5期
782-789
,共8页
城市绿地%面向对象分类%两阶段分类%LiDAR%高分辨率遥感影像
城市綠地%麵嚮對象分類%兩階段分類%LiDAR%高分辨率遙感影像
성시록지%면향대상분류%량계단분류%LiDAR%고분변솔요감영상
urban vegetation%object-based classification%two-stage classification%LiDAR%high resolution remote sensing image
提出了一种新的面向对象的城市绿地信息两阶段提取方法.该方法分阶段使用高分辨率遥感影像的光谱和2维形态信息以及机载LiDAR数据的3维形态信息作为分类依据.第1阶段,影像首先被分割为对象,对象被分类为无阴影的植被、阴影下的植被、水体、建筑物、空地和阴影6类地物;无阴影的植被和阴影下的植被合并为城市绿地对象,在第2阶段,将LiDAR数据产生的归一化数字表面模型nDSM与绿地对象叠加,计算每个对象的3维形态属性,进一步将绿地对象细分为草坪、灌木和乔木.以美国休斯敦中心城区为例,介绍了方法流程.精度分析表明,绿地的分类精度达到93.46%;方法中的主要误差来源于遥感影像当中的建筑物阴影以及生成数字地形模型时所产生的误差.
提齣瞭一種新的麵嚮對象的城市綠地信息兩階段提取方法.該方法分階段使用高分辨率遙感影像的光譜和2維形態信息以及機載LiDAR數據的3維形態信息作為分類依據.第1階段,影像首先被分割為對象,對象被分類為無陰影的植被、陰影下的植被、水體、建築物、空地和陰影6類地物;無陰影的植被和陰影下的植被閤併為城市綠地對象,在第2階段,將LiDAR數據產生的歸一化數字錶麵模型nDSM與綠地對象疊加,計算每箇對象的3維形態屬性,進一步將綠地對象細分為草坪、灌木和喬木.以美國休斯敦中心城區為例,介紹瞭方法流程.精度分析錶明,綠地的分類精度達到93.46%;方法中的主要誤差來源于遙感影像噹中的建築物陰影以及生成數字地形模型時所產生的誤差.
제출료일충신적면향대상적성시록지신식량계단제취방법.해방법분계단사용고분변솔요감영상적광보화2유형태신식이급궤재LiDAR수거적3유형태신식작위분류의거.제1계단,영상수선피분할위대상,대상피분류위무음영적식피、음영하적식피、수체、건축물、공지화음영6류지물;무음영적식피화음영하적식피합병위성시록지대상,재제2계단,장LiDAR수거산생적귀일화수자표면모형nDSM여록지대상첩가,계산매개대상적3유형태속성,진일보장록지대상세분위초평、관목화교목.이미국휴사돈중심성구위례,개소료방법류정.정도분석표명,록지적분류정도체도93.46%;방법중적주요오차래원우요감영상당중적건축물음영이급생성수자지형모형시소산생적오차.
The urban vegetation is a principal biological component of the urban landscape.Identifying and mapping the urban vegetation are important to urban management and planning.This paper presents a new object-based two-stage method to classify urban vegetation using airborne LiDAR data and high resolution aerial photographs through a case study of downtown Houston,USA.By exploiting the spectral information plus 2D geometric attributes from high resolution aerial photographs and 3D morphological information from airborne LiDAR data,a detailed and accurate classification of urban vegetation has been achieved.In the first stage,the aerial photographs are segmented into image objects.Based on the spectral and 2D geometric attributes,these objects are divided into six categories:non-shaded vegetation,shaded vegetation,water,building,open space,and shade.Vegetation objects,including non-shaded and shaded vegetation,Slag derived separately.In the second stage,the normalized Digital Surface Model derived from airborne LiDAR data is introduced to characterize the 3D geometric properties(height and roughness)of each vegetation object.Based on these.properties,the vegetation objects are further classified into trees,shrubs/hedges,and grass-covered lawns.The overall classification accuracy of vegetation is analyzed and reported as high as 93.46%.The sources of errors are ascribed to the shade in aerial photo and the miscalculation of Digital Terrain Model from LiDAR data.This research suggests that the combination of morphological information of LiDAR and the spectral information from image data renders a powerful tool for a detailed investigation of urban vegetation.