Multiple-scale object-oriented building extraction method from high resolution image
Multiple-scale object-oriented building extraction method from high resolution image
复制标题
高分辨率图像中多尺度面向对象的建筑物提取方法
DOI：
10.30918/ajer.61.17.026
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Q. Zhou
中科院分区：
文献类型：
--
作者：
C. Li;J. Fang;J. Chen;Q. Zhou
本文基于高分辨率遥感数据和e-Cognition Developer平台，充分利用高分辨率QuickBird影像丰富的光谱、空间、纹理和几何信息，成功地对建筑物区域进行分类。采用面向对象的多尺度分割方法和最近邻隶属函数分类方法，将研究区划分为居住建筑、绿色空间、道路、休闲区和裸地5个用地类别。在此基础上，最终提取出了住宅建筑物信息。实验结果表明：与传统的逐像元分类方法相比，本文提出的面向对象分类方法能有效避免分割区域的碎片化，在土地分类中更加完整、准确和高效。
Based on high-resolution remote sensing data and the e-Cognition Developer platform, in this paper, we make full use of rich spectral, spatial, texture and geometry information of high resolution QuickBird images in order to classify building areas successfully. The object-oriented multiple-scale segmentation method and the nearest neighborhood and membership function classification method are applied to classify the study area into five land categories; they are residential building, green space, road, leisure area and bare area respectively. On the basis above, the residential building information is extracted eventually. The experiment results show that: compared with the conventional pixel-by-pixel classification method, the object-oriented classification method proposed in this paper can effectively avoid the fragmentation of the segmented regions, which is more complete, accurate and efficient in land classification.