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Article: Examining the effect of spatial resolution and texture window size on classification accuracy: An urban environment case
Title | Examining the effect of spatial resolution and texture window size on classification accuracy: An urban environment case |
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Authors | |
Issue Date | 2004 |
Citation | International Journal of Remote Sensing, 2004, v. 25, n. 11, p. 2177-2192 How to Cite? |
Abstract | The purpose of this paper is to evaluate spatial resolution effects on image classification. Classification maps were generated with a maximum likelihood (ML) classifier applied to three multi-spectral bands and variance texture images. A total of eight urban land use/cover classes were obtained at six spatial resolution levels based on a series of aggregated Colour Infrared Digital Orthophoto Quarter Quadrangle (DOQQ) subsets in urban and rural fringe areas of the San Diego metropolitan area. The classification results were compared using overall and individual classification accuracies. Classification accuracies were shown to be influenced by image spatial resolution, window size used in texture extraction and differences in spatial structure within and between categories. The more heterogeneous are the land use/cover units and the more fragmented are the landscapes, the finer the resolution required. Texture was more effective for improving the classification accuracy of land use classes at finer resolution levels. For spectrally homogeneous classes, a small window is preferable. But for spectrally heterogeneous classes, a large window size is required. © 2004 Taylor & Francis Ltd. |
Persistent Identifier | http://hdl.handle.net/10722/296579 |
ISSN | 2023 Impact Factor: 3.0 2023 SCImago Journal Rankings: 0.776 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Chen, D. | - |
dc.contributor.author | Stow, D. A. | - |
dc.contributor.author | Gong, P. | - |
dc.date.accessioned | 2021-02-25T15:16:12Z | - |
dc.date.available | 2021-02-25T15:16:12Z | - |
dc.date.issued | 2004 | - |
dc.identifier.citation | International Journal of Remote Sensing, 2004, v. 25, n. 11, p. 2177-2192 | - |
dc.identifier.issn | 0143-1161 | - |
dc.identifier.uri | http://hdl.handle.net/10722/296579 | - |
dc.description.abstract | The purpose of this paper is to evaluate spatial resolution effects on image classification. Classification maps were generated with a maximum likelihood (ML) classifier applied to three multi-spectral bands and variance texture images. A total of eight urban land use/cover classes were obtained at six spatial resolution levels based on a series of aggregated Colour Infrared Digital Orthophoto Quarter Quadrangle (DOQQ) subsets in urban and rural fringe areas of the San Diego metropolitan area. The classification results were compared using overall and individual classification accuracies. Classification accuracies were shown to be influenced by image spatial resolution, window size used in texture extraction and differences in spatial structure within and between categories. The more heterogeneous are the land use/cover units and the more fragmented are the landscapes, the finer the resolution required. Texture was more effective for improving the classification accuracy of land use classes at finer resolution levels. For spectrally homogeneous classes, a small window is preferable. But for spectrally heterogeneous classes, a large window size is required. © 2004 Taylor & Francis Ltd. | - |
dc.language | eng | - |
dc.relation.ispartof | International Journal of Remote Sensing | - |
dc.title | Examining the effect of spatial resolution and texture window size on classification accuracy: An urban environment case | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1080/01431160310001618464 | - |
dc.identifier.scopus | eid_2-s2.0-2942586682 | - |
dc.identifier.volume | 25 | - |
dc.identifier.issue | 11 | - |
dc.identifier.spage | 2177 | - |
dc.identifier.epage | 2192 | - |
dc.identifier.isi | WOS:000221428900012 | - |
dc.identifier.issnl | 0143-1161 | - |