Application of Feature Selection Method in Building Information Extracting from High Resolution Remote Sensing Image

doi:10.13474/j.cnki.11-2246.2018.0058

Abstract

Abstract:

The automatic extraction of urban building information is one of the key technologies of urban remote sensing,however,the accuracy of extraction is often unstable due to many factors such as shadows and undersides.Taking the Pleiades satellite image as a basic data source,the paper explores the feasibility of urban building information extraction by advanced Relief F feature selection method.Firstly,the basic feature space of buildings with high resolution remote sensing image is built.Then,the optimal characteristics are selected by the weights which are determined by advanced Relief F algorithm.Finally,the supervised classification,the non-feature selection classification,and the advanced Relief F feature selection methods are respectively used to extract the building information in the study area,and the accuracy of the extraction is verified and compared by site investigation data.The result shows that the advanced Relief F method can reach a higher accuracy with 91.34% and a higher extraction speed with 34.31% and 5.62% higher than the other two methods.The result demonstrates that the advanced Relief F feature selection method has certain reliability and applicability,and can make the work of building information extraction become more automatic and intelligent.

Key words: urban remote sensing, building information, feature selection, Relief F, Pleiades satellite image

CLC Number:

P237

LIU Run, ZHANG Shaoliang, JIA Rong. Application of Feature Selection Method in Building Information Extracting from High Resolution Remote Sensing Image[J]. 测绘通报, 2018, 0(2): 126-130.

References

[1] CHRISTODOULIDIS A,HURTUT T,TAHAR H B,et al.A Multi-scale Tensor Voting Approach for Small Retinal Vessel Segmentation in High Resolution Fundus Images[J].Computerized Medical Imaging and Graphics,2016,52(9):28-43.
[2] 胡荣明,黄小兵,黄远程.增强形态学建筑物指数应用于高分辨率遥感影像中建筑物提取[J].测绘学报,2014,43(5):514-520.
[3] 李朝奎,方文,董小姣.面向对象和规则的高分辨率影像分类研究[J].测绘通报,2015(9):9-13,35.
[4] 张庆云,赵冬.高空间分辨率遥感影像建筑物提取方法综述[J].测绘与空间地理信息,2015(4):74-78.
[5] NEVATIA R,LIN C,HUERTAS A.A System for Building Detection from Aerial Images[C]//Proceeding of Automatic Extraction of Man-made Object from Aerial and Space image(Ⅱ).Basel:Brikhauser,1997:77-86.
[6] HUERTAS A,NEVATIA R.Detecting Buildings in Aerial Images[J].Computer Vision,Graphics and Image Processing,1988,41(2):131-152.
[7] 唐亮,谢维信,黄建军,等.直线Snakes及其在建筑物提取中的应用[J].西安电子科技大学学报,2005,32(1):60-65.
[8] BAATZ M,SCHAPE A.Object-oriented and Multi-scale Image Analysis in Semantic Networks[C]//Proceedings of the 2nd International Symposium on Operationalization of Remote Sensing.Enschede:ITC,1999.
[9] 吕凤华,舒宁,龚颵,等.利用多特征进行航空影像建筑物提取[J].武汉大学学报(信息科学版),2017,42(5):656-660.
[10] 周绍光,孙金彦,凡莉,等.高分辨率遥感影像的建筑物轮廓信息提取方法[J].国土资源遥感,2015,27(3):52-58.
[11] KIRA K,RENDELL L.The Feature Selection Problem:Traditional Methods and a New Algorithm[C]//Proceedings of the Ninth National Conference on Artificial Intelligence.New Orleans:AAAI Press,1992:129-134.
[12] 李晓岚.基于Relief特征选择算法的研究与应用[D].大连:大连理工大学,2013.
[13] KONONENKO I.Estimating Attributes:Analysis and Extension of Relief[J].Machine Learning,1994,784(4):171-182.
[14] CHENG F,THIEL K H.Delimiting the Building Heights in a City from the Shadow in a Panchromatic SPOT-image-Part 1.Test of Forty-two Buildings[J].International Journal of Remote Sensing,1995,16(3):409-415.
[15] 付卓.高分辨率遥感卫星影像中城市人工地物提取方法研究[D].北京:北京交通大学,2006.
[16] 陶阳宇,梁林,徐迎庆,等.利用贝叶斯结构模型的复杂形状建筑物提取[J].计算机辅助设计与图形学学报,2010,22(4):647-653.