Building area extraction from the high spatial resolution remote sensing imagery

被引:5
|
作者
Shi, Wenzao [1 ,2 ,3 ]
Mao, Zhengyuan [4 ,5 ,6 ]
Liu, Jinqing [1 ,2 ,3 ]
机构
[1] Fujian Normal Univ, Fujian Prov Engn Technol Res Ctr Photoelect Sensi, Fuzhou 350117, Fujian, Peoples R China
[2] Fujian Normal Univ, Fujian Prov Key Lab Photon Technol, Minist Educ, Key Lab OptoElect Sci & Technol Med, Fuzhou 350007, Fujian, Peoples R China
[3] Fujian Normal Univ, Coll Photon & Elect Engn, Fuzhou 350007, Fujian, Peoples R China
[4] Fuzhou Univ, Minist Educ, Key Lab Spatial Data Min & Informat Sharing, Fuzhou 350002, Fujian, Peoples R China
[5] Fuzhou Univ, Natl Engn Res Ctr Geospatial Informat Technol, Fuzhou 350002, Fujian, Peoples R China
[6] Fuzhou Univ, Spatial Informat Engn Res Ctr Fujian Prov, Fuzhou 350002, Fujian, Peoples R China
基金
中国国家自然科学基金;
关键词
High-resolution remote sensing imagery; Building area extraction; Gradient; URBAN AREAS; AERIAL IMAGES; CLASSIFICATION; SEGMENTATION; INDEX;
D O I
10.1007/s12145-018-0355-5
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
An approach to building area extraction from high-resolution remote sensing imagery is proposed based on the local gradient orientation density function (LGODF) and the bimodal density function (BDF). Firstly, the LGODF is calculated by moving the window of 35x35 based on the gradient magnitude and the gradient direction of each pixel in the image. Then, the BDF is obtained by multiplying the movable bimodal Gaussian mixture function and LGODF in each window. Finally, peaks with difference of 90 degrees are searched for in the BDF, and the central point of the corresponding windows are determined as building pixels. For the validity of the proposed method, seven representative sub-images from PLEIADES images covering Shenzhen China are selected. Experimental results reveal that the precision achieve 94.08% and the recall up to 96.70%.
引用
收藏
页码:19 / 29
页数:11
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