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- Publisher Website: 10.1109/WHISPERS.2016.8071729
- Scopus: eid_2-s2.0-85037539207
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Conference Paper: High-level impervious surfaces classification in urban environments from hyperspectral imagery
Title | High-level impervious surfaces classification in urban environments from hyperspectral imagery |
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Authors | |
Keywords | Classification Hyperspectral Impervious surfaces |
Issue Date | 2017 |
Citation | Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing, 2017 How to Cite? |
Abstract | © 2016 IEEE. Remote sensing techniques have great potential in providing accurate and timely information in urban areas. Estimation of impervious surfaces (IS) is rousing widely interests of researchers in monitoring urban development and determining the overall environmental health of a watershed. However, study on IS is complicated due to the complexity of urban infrastructures which include considerable spectral diversity when conduct research at very fine spatial scales. While traditional multispectral remote sensing data is inadequate in dealing with high-level IS estimation. With the urgent needs of testing the capability of hyperspectral imageries in further recognizing different materials of estimated IS, an image equipped with both high spatial and spectral resolution is adopted to demonstrate the whole workflow in using high dimensional data for high-level impervious surface classification, including determining the number of sub-classes and selecting spectrally similar training samples. |
Persistent Identifier | http://hdl.handle.net/10722/277679 |
ISSN | 2020 SCImago Journal Rankings: 0.174 |
DC Field | Value | Language |
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dc.contributor.author | Wang, Ting | - |
dc.contributor.author | Zhang, Hongsheng | - |
dc.contributor.author | Lin, Hui | - |
dc.date.accessioned | 2019-09-27T08:29:41Z | - |
dc.date.available | 2019-09-27T08:29:41Z | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing, 2017 | - |
dc.identifier.issn | 2158-6276 | - |
dc.identifier.uri | http://hdl.handle.net/10722/277679 | - |
dc.description.abstract | © 2016 IEEE. Remote sensing techniques have great potential in providing accurate and timely information in urban areas. Estimation of impervious surfaces (IS) is rousing widely interests of researchers in monitoring urban development and determining the overall environmental health of a watershed. However, study on IS is complicated due to the complexity of urban infrastructures which include considerable spectral diversity when conduct research at very fine spatial scales. While traditional multispectral remote sensing data is inadequate in dealing with high-level IS estimation. With the urgent needs of testing the capability of hyperspectral imageries in further recognizing different materials of estimated IS, an image equipped with both high spatial and spectral resolution is adopted to demonstrate the whole workflow in using high dimensional data for high-level impervious surface classification, including determining the number of sub-classes and selecting spectrally similar training samples. | - |
dc.language | eng | - |
dc.relation.ispartof | Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing | - |
dc.subject | Classification | - |
dc.subject | Hyperspectral | - |
dc.subject | Impervious surfaces | - |
dc.title | High-level impervious surfaces classification in urban environments from hyperspectral imagery | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/WHISPERS.2016.8071729 | - |
dc.identifier.scopus | eid_2-s2.0-85037539207 | - |
dc.identifier.spage | null | - |
dc.identifier.epage | null | - |
dc.identifier.issnl | 2158-6268 | - |