An Object Extraction Approach for Impervious Surface Classification with Very-High-Resolution Imagery

Detailed land cover maps provide important information for research and decision-making but are often expensive to develop and can become outdated quickly. Widespread availability of aerial photography provides increased accessibility of high-resolution imagery and the potential to produce high-accu...

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Bibliographic Details
Published in:The Professional geographer 2009-05, Vol.61 (2), p.250-264
Main Authors: Miller, Jennifer Elizabeth, Nelson, Stacy A. C., Hess, George R.
Format: Article
Language:English
Subjects:
Accuracy
Aerial photography
Aerial surveying
Bgi / Prodig
Cartography, space figuration
cobertura de la tierra
Decision making
Feature Analyst
fotografía aérea
Geographic information systems
Geographical information systems
Geography
GIS
Image interpreation. Remote sensing
impervious surface
Land
land cover
North Carolina
Photography
SIG
superficie impermeable
U.S.A
不可渗透表面
土地覆盖
地理信息系统
特征分析
航空摄影
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Summary:Detailed land cover maps provide important information for research and decision-making but are often expensive to develop and can become outdated quickly. Widespread availability of aerial photography provides increased accessibility of high-resolution imagery and the potential to produce high-accuracy land cover classifications. However, these classifications often require expert knowledge and are time consuming. Our goal was to develop an efficient, accurate technique for classifying impervious surface in urbanizing Wake County, North Carolina. Using an iterative training technique, we classified 111 nonmosaicked, very-high-resolution images using the Feature Analyst software developed by Visual Learning Systems. Feature Analyst provides object extraction classifications by analyzing spatial context in relation to spectral data to classify high-resolution imagery. Our image classification results were 95 percent accurate in impervious surface extraction, with an overall total accuracy of 92 percent. Using this method, users with relatively limited geographic information system (GIS) training and modest budgets can produce highly accurate object-extracted classifications of impervious and pervious surface that are easily manipulated in a GIS.
ISSN:0033-0124
1467-9272
DOI:10.1080/00330120902742920