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Article: Study of urban spatial patterns from SPOT panchromatic imagery using textural analysis

TitleStudy of urban spatial patterns from SPOT panchromatic imagery using textural analysis
Authors
Issue Date2003
Citation
International Journal of Remote Sensing, 2003, v. 24, n. 21, p. 4137-4160 How to Cite?
AbstractThe long-time historical evolution and recent rapid development of Beijing, China, present before us a unique urban structure. A 10-metre spatial resolution SPOT panchromatic image of Beijing has been studied to capture the spatial patterns of the city. Supervised image classifications were performed using statistical and structural texture features produced from the image. Textural features, including eight texture features from the Grey-Level Co-occurrence Matrix (GLCM) method; a computationally efficient texture feature, the Number of Different Grey-levels (NDG); and a structural texture feature, Edge Density (ED), were evaluated. It was found that generally single texture features performed poorly. Classification accuracy increased with increasing number of texture features until three or four texture features were combined. The more texture features in the combination, the smaller difference between different combinations. The results also show that a lower number of texture features were needed for more homogeneous areas. NDG and ED combined with GLCM texture features produced similar results as the same number of GLCM texture features. Two classification schemes were adopted, stratified classification and non-stratified classification. The best stratified classification result was better than the best non-stratified classification result.
Persistent Identifierhttp://hdl.handle.net/10722/296551
ISSN
2020 Impact Factor: 3.151
2020 SCImago Journal Rankings: 0.918
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorZhang, Q.-
dc.contributor.authorWang, J.-
dc.contributor.authorGong, P.-
dc.contributor.authorShi, P.-
dc.date.accessioned2021-02-25T15:16:08Z-
dc.date.available2021-02-25T15:16:08Z-
dc.date.issued2003-
dc.identifier.citationInternational Journal of Remote Sensing, 2003, v. 24, n. 21, p. 4137-4160-
dc.identifier.issn0143-1161-
dc.identifier.urihttp://hdl.handle.net/10722/296551-
dc.description.abstractThe long-time historical evolution and recent rapid development of Beijing, China, present before us a unique urban structure. A 10-metre spatial resolution SPOT panchromatic image of Beijing has been studied to capture the spatial patterns of the city. Supervised image classifications were performed using statistical and structural texture features produced from the image. Textural features, including eight texture features from the Grey-Level Co-occurrence Matrix (GLCM) method; a computationally efficient texture feature, the Number of Different Grey-levels (NDG); and a structural texture feature, Edge Density (ED), were evaluated. It was found that generally single texture features performed poorly. Classification accuracy increased with increasing number of texture features until three or four texture features were combined. The more texture features in the combination, the smaller difference between different combinations. The results also show that a lower number of texture features were needed for more homogeneous areas. NDG and ED combined with GLCM texture features produced similar results as the same number of GLCM texture features. Two classification schemes were adopted, stratified classification and non-stratified classification. The best stratified classification result was better than the best non-stratified classification result.-
dc.languageeng-
dc.relation.ispartofInternational Journal of Remote Sensing-
dc.titleStudy of urban spatial patterns from SPOT panchromatic imagery using textural analysis-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1080/0143116031000070445-
dc.identifier.scopuseid_2-s2.0-0344099114-
dc.identifier.volume24-
dc.identifier.issue21-
dc.identifier.spage4137-
dc.identifier.epage4160-
dc.identifier.isiWOS:000186177900006-
dc.identifier.issnl0143-1161-

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