Automated Building Extraction from High-Resolution Satellite Imagery in Urban Areas Using Structural, Contextual, and Spectral Information
<p/> <p>High-resolution satellite imagery provides an important new data source for building extraction. We demonstrate an integrated strategy for identifying buildings in 1-meter resolution satellite imagery of urban areas. Buildings are extracted using structural, contextual, and spect...
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Online Access: | http://dx.doi.org/10.1155/ASP.2005.2196 |
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doaj-47c1c9e058e643a99f9ee5054cf66de72020-11-24T21:33:40ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61721687-61802005-01-01200514745309Automated Building Extraction from High-Resolution Satellite Imagery in Urban Areas Using Structural, Contextual, and Spectral InformationJin XiaoyingDavis Curt H<p/> <p>High-resolution satellite imagery provides an important new data source for building extraction. We demonstrate an integrated strategy for identifying buildings in 1-meter resolution satellite imagery of urban areas. Buildings are extracted using structural, contextual, and spectral information. First, a series of geodesic opening and closing operations are used to build a differential morphological profile (DMP) that provides image structural information. Building hypotheses are generated and verified through shape analysis applied to the DMP. Second, shadows are extracted using the DMP to provide reliable contextual information to hypothesize position and size of adjacent buildings. Seed building rectangles are verified and grown on a finely segmented image. Next, bright buildings are extracted using spectral information. The extraction results from the different information sources are combined after independent extraction. Performance evaluation of the building extraction on an urban test site using IKONOS satellite imagery of the City of Columbia, Missouri, is reported. With the combination of structural, contextual, and spectral information, <inline-formula><graphic file="1687-6180-2005-745309-i1.gif"/></inline-formula> of the building areas are extracted with a quality percentage <inline-formula><graphic file="1687-6180-2005-745309-i2.gif"/></inline-formula>.</p>http://dx.doi.org/10.1155/ASP.2005.2196building extractionhigh-resolution satellite imagerymathematical morphologyshadowhypothesis and verificationinformation fusion |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jin Xiaoying Davis Curt H |
spellingShingle |
Jin Xiaoying Davis Curt H Automated Building Extraction from High-Resolution Satellite Imagery in Urban Areas Using Structural, Contextual, and Spectral Information EURASIP Journal on Advances in Signal Processing building extraction high-resolution satellite imagery mathematical morphology shadow hypothesis and verification information fusion |
author_facet |
Jin Xiaoying Davis Curt H |
author_sort |
Jin Xiaoying |
title |
Automated Building Extraction from High-Resolution Satellite Imagery in Urban Areas Using Structural, Contextual, and Spectral Information |
title_short |
Automated Building Extraction from High-Resolution Satellite Imagery in Urban Areas Using Structural, Contextual, and Spectral Information |
title_full |
Automated Building Extraction from High-Resolution Satellite Imagery in Urban Areas Using Structural, Contextual, and Spectral Information |
title_fullStr |
Automated Building Extraction from High-Resolution Satellite Imagery in Urban Areas Using Structural, Contextual, and Spectral Information |
title_full_unstemmed |
Automated Building Extraction from High-Resolution Satellite Imagery in Urban Areas Using Structural, Contextual, and Spectral Information |
title_sort |
automated building extraction from high-resolution satellite imagery in urban areas using structural, contextual, and spectral information |
publisher |
SpringerOpen |
series |
EURASIP Journal on Advances in Signal Processing |
issn |
1687-6172 1687-6180 |
publishDate |
2005-01-01 |
description |
<p/> <p>High-resolution satellite imagery provides an important new data source for building extraction. We demonstrate an integrated strategy for identifying buildings in 1-meter resolution satellite imagery of urban areas. Buildings are extracted using structural, contextual, and spectral information. First, a series of geodesic opening and closing operations are used to build a differential morphological profile (DMP) that provides image structural information. Building hypotheses are generated and verified through shape analysis applied to the DMP. Second, shadows are extracted using the DMP to provide reliable contextual information to hypothesize position and size of adjacent buildings. Seed building rectangles are verified and grown on a finely segmented image. Next, bright buildings are extracted using spectral information. The extraction results from the different information sources are combined after independent extraction. Performance evaluation of the building extraction on an urban test site using IKONOS satellite imagery of the City of Columbia, Missouri, is reported. With the combination of structural, contextual, and spectral information, <inline-formula><graphic file="1687-6180-2005-745309-i1.gif"/></inline-formula> of the building areas are extracted with a quality percentage <inline-formula><graphic file="1687-6180-2005-745309-i2.gif"/></inline-formula>.</p> |
topic |
building extraction high-resolution satellite imagery mathematical morphology shadow hypothesis and verification information fusion |
url |
http://dx.doi.org/10.1155/ASP.2005.2196 |
work_keys_str_mv |
AT jinxiaoying automatedbuildingextractionfromhighresolutionsatelliteimageryinurbanareasusingstructuralcontextualandspectralinformation AT daviscurth automatedbuildingextractionfromhighresolutionsatelliteimageryinurbanareasusingstructuralcontextualandspectralinformation |
_version_ |
1725952687103541248 |