NDLI: Urban land cover classification based on WorldView-2 image data

Content Provider	IEEE Xplore Digital Library
Author	Zhiyong Chen Xiaogang Ning Jixian Zhang
Copyright Year	2012
Description	Author affiliation: School of Math, Physics and Software Engineering, Lanzhou Jiaotong University, Gansu, China (Zhiyong Chen) \|\| Institue of Photogrammety and Remote Sensing, Chinese Academy of Surverying & Mapping, Beijing, China (Xiaogang Ning; Jixian Zhang)
Abstract	Cities hahave a complex construction and easily affected by human activities, so they needsto be surveyed and analyzed timely. WorldView-2 high resolution remote sensing image makes it possible to study the urban land cover classification by its abundant space geometric features and spectral information. This paper would have aimed at given urban land cover types to choose suitable segmentation scales and classification features through object oriented multi-scale segmentation and classification method based on WorldView-2 image data, and extracted the urban land cover types progressively according to reasonable order. Then it raised the NDWI and NDVI which appropriated to extract water and vegetation on WorldView-2 image, and grouped the objects' features after segmentation to extract the roads and buildings hierarchically. The results of accuracy assessment indicated that using this method to study the urban land cover classification based on WorldView-2 image received an ideal effect.
Starting Page	1
Ending Page	5
File Size	426112
Page Count	5
File Format	PDF
ISBN	9781467312837
e-ISBN	9781467312820
DOI	10.1109/GIWRM.2012.6349578
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-10-19
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Multi-scale segmentation Image segmentation Image resolution Roads Buildings Vegetation mapping Feature extraction WorldView-2 high resolution remote sensing image Remote sensing Object-oriented
Content Type	Text
Resource Type	Article

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