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Article: Settlement extraction in the North China Plain using Landsat and Beijing-1 multispectral data with an improved watershed segmentation algorithm

TitleSettlement extraction in the North China Plain using Landsat and Beijing-1 multispectral data with an improved watershed segmentation algorithm
Authors
Issue Date2010
Citation
International Journal of Remote Sensing, 2010, v. 31, n. 6, p. 1411-1426 How to Cite?
AbstractIn this paper we present an improved watershed segmentation algorithm for settlement mapping from medium resolution satellite data over plain areas in China. The algorithm can increase the computational efficiency of the fastest reported watershed segmentation algorithm by 30-40%. We apply this method to a selected study area in southern Hebei Province, China. We acquired a Landsat Enhanced Thematic Mapper Plus (ETM+) image over this area in May 2000, two Landsat Thematic Mapper (TM) images in August 2004 and April 2005, and two Beijing-1 satellite images in May 2006 and May 2007. The three types of images have three similar spectral bands (green, red and near-infrared) with similar spatial resolution (30-32 m). Only the red and near-infrared bands were used in image segmentation for settlement area extraction. The extracted settlement results are compared with manual interpretation results by two people. We assumed the human interpretation results are of higher accuracy than the segmentation results. Our results indicated that our settlement area extraction method is effective. With high quality images, the overall accuracies are nearly 94%, the kappa coefficient can be greater than 0.85. © 2010 Taylor & Francis.
Persistent Identifierhttp://hdl.handle.net/10722/296661
ISSN
2023 Impact Factor: 3.0
2023 SCImago Journal Rankings: 0.776
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorWang, Lei-
dc.contributor.authorGong, Peng-
dc.contributor.authorYing, Qing-
dc.contributor.authorYang, Zhenzhong-
dc.contributor.authorCheng, Xiao-
dc.contributor.authorRan, Qiong-
dc.date.accessioned2021-02-25T15:16:23Z-
dc.date.available2021-02-25T15:16:23Z-
dc.date.issued2010-
dc.identifier.citationInternational Journal of Remote Sensing, 2010, v. 31, n. 6, p. 1411-1426-
dc.identifier.issn0143-1161-
dc.identifier.urihttp://hdl.handle.net/10722/296661-
dc.description.abstractIn this paper we present an improved watershed segmentation algorithm for settlement mapping from medium resolution satellite data over plain areas in China. The algorithm can increase the computational efficiency of the fastest reported watershed segmentation algorithm by 30-40%. We apply this method to a selected study area in southern Hebei Province, China. We acquired a Landsat Enhanced Thematic Mapper Plus (ETM+) image over this area in May 2000, two Landsat Thematic Mapper (TM) images in August 2004 and April 2005, and two Beijing-1 satellite images in May 2006 and May 2007. The three types of images have three similar spectral bands (green, red and near-infrared) with similar spatial resolution (30-32 m). Only the red and near-infrared bands were used in image segmentation for settlement area extraction. The extracted settlement results are compared with manual interpretation results by two people. We assumed the human interpretation results are of higher accuracy than the segmentation results. Our results indicated that our settlement area extraction method is effective. With high quality images, the overall accuracies are nearly 94%, the kappa coefficient can be greater than 0.85. © 2010 Taylor & Francis.-
dc.languageeng-
dc.relation.ispartofInternational Journal of Remote Sensing-
dc.titleSettlement extraction in the North China Plain using Landsat and Beijing-1 multispectral data with an improved watershed segmentation algorithm-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1080/01431160903475332-
dc.identifier.scopuseid_2-s2.0-77951111555-
dc.identifier.volume31-
dc.identifier.issue6-
dc.identifier.spage1411-
dc.identifier.epage1426-
dc.identifier.eissn1366-5901-
dc.identifier.isiWOS:000277389100004-
dc.identifier.issnl0143-1161-

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