One Method of Urban Land Covers Information Extraction

Article Preview

Abstract:

To study urban land cover information extracting method used one scene LANDSAT-TM remote sensing image of Chongqing city, China. Since NDVI calculation can enhance vegetation information, a calculation expression which is built according to water spectrum feather can enhance water information, and NDBI (Normalization Difference Building Index) calculation can enhance impervious surface information, the three calculations were used to get three thematic images, and after stacking them on to be one image, supervised classification used k-Nearest Neighbor algorithm based on Object-Oriented features of the image was used to get vegetation , water and impervious surface information. To value the final accuracy of the classification, 100 random sampling points were chosen and the high spatial resolution remote sensing image of Google Earth was taken to be the reference, the overall classification accuracy is 83.4%, and the Kappa statistic is 0.814.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

4011-4014

Citation:

Online since:

August 2013

Authors:

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2013 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] Zhang Youjing, Gao Yunxiao, et al. Research on remote sensing classification of urban vegetation species on SVM decision making tree[J]. Journal of Remote Sensing, 2006, 10(2): 191-199.

Google Scholar

[2] WANIG Bi-hui, WU Yun-chao. HUANG X2iao-chun Urban Land-use Classification Using High Resolution Remote Sensing Data[J]. Remote Sensing Information, 2012, 27(4): 111-117.

Google Scholar

[3] Zhu W Q, et a1.Quantitative Analysis of Urban Forests Structure[J].Chinese Journal of Applied Ecology, 2003, (14):2090-2094.

Google Scholar

[4] Hu Jingang, Zhang xiaodong, Shen xin, Zhang Chan. A Method 0f Road Extraction in High—Resolution Remote Sensing Imagery Based on Object—oriented Image Analysis[J]. Remote Sensing Technology And Application, 2006, 3(21): 184-190.

DOI: 10.3390/rs11212499

Google Scholar

[5] Yang Yujing, Feng Jjianhui. Research On Extraction and Assistant Classification of Remote Sensing for Texture Feature[J]. Hydrographic Surveying And Charting, 2008, 28(4):37—40.

Google Scholar

[6] Wu Guiping, Zeng Yongnian. Dynamic Simulation Of Land Use Change Based On The Improved CLUE-S Model : A case study of Yongding County , Zhangjiajie[J]. Geographical Research, 2010, 29(3):460-470.

Google Scholar

[7] Yang Shuwen, Xue Chongsheng, Liu Tao, Li Yikun. A Method Of Small Water Information Automatic Extraction From TM Remote Sensing Images[J]. Acta Geodaetica Et Cartographica Sinica, 2010, 39(6): 611-617.

Google Scholar

[8] Zha Yong, Ni shaoxiang, Yang Shan. An Effective Approach to Automatically Extract Urban Land-use from TM Imagery [J]. Journal Of Remote Sensing, 2003, 7(1): 37-40.

Google Scholar