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Spectral index to improve the extraction of built-up area from WorldView-2 imagery

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dc.contributor.author Adeyemi, A
dc.contributor.author Ramoelo, A
dc.contributor.author Cho, Moses A
dc.contributor.author Masemola, C
dc.date.accessioned 2021-08-30T07:50:39Z
dc.date.available 2021-08-30T07:50:39Z
dc.date.issued 2021-04
dc.identifier.citation Adeyemi, A., Ramoelo, A., Cho, M.A. & Masemola, C. 2021. Spectral index to improve the extraction of built-up area from WorldView-2 imagery. <i>Journal of Applied Remote Sensing, 5(2).</i> http://hdl.handle.net/10204/12103 en_ZA
dc.identifier.issn 1931-3195
dc.identifier.uri https://doi.org/10.1117/1.JRS.15.024510
dc.identifier.uri http://hdl.handle.net/10204/12103
dc.description.abstract Globally, the unprecedented increase in population in many cities has led to rapid changes in urban landscape, which requires timely assessments and monitoring. Accurate determination of built-up information is vital for urban planning and environmental management. Often, the determination of the built-up area information has been dependent on field surveys, which is laborious and time-consuming. Remote sensing data are the only option for deriving spatially explicit and timely built-up area information. There are few spectral indices for built-up areas and often not accurate as they are specific to impervious material, age, colour, and thickness, especially using higher resolution images. The objective of this study is to test the utility of a new built-up extraction index (NBEI) using WorldView-2 (WV-2) to improve built-up material mapping irrespective of material type, age, and color. The new index was derived from spectral bands such as green, red edge, NIR1, and NIR2 bands that profoundly explain the variation in built-up areas on WV-2 image. The result showed that NBEI improves the extraction of built-up areas with high accuracy [area under the receiver operating characteristic curve,   (  AUROC  )    =    ∼  0.82] compared to the existing indices such as built-up area index (AUROC  =    ∼  0.73), built-up spectral index (AUROC  =    ∼  0.78), red edge/green index (AUROC  =    ∼  0.71) and WorldView-Built-up Index (WV-BI) (AUROC  =    ∼  0.67). The study demonstrated that the new built-up index could extract built-up areas using high-resolution images. The performance of NBEI could be attributed to the fact that it is not material-specific, and would be necessary for urban area mapping. en_US
dc.format Abstract en_US
dc.language.iso en en_US
dc.relation.uri https://www.spiedigitallibrary.org/journals/journal-of-applied-remote-sensing/volume-15/issue-02/024510/Spectral-index-to-improve-the-extraction-of-built-up-area/10.1117/1.JRS.15.024510.full en_US
dc.source Journal of Applied Remote Sensing, 5(2) en_US
dc.subject Built-up en_US
dc.subject Spectral indices en_US
dc.subject Very high resolution en_US
dc.subject WorldView-2 en_US
dc.title Spectral index to improve the extraction of built-up area from WorldView-2 imagery en_US
dc.type Article en_US
dc.description.pages 19pp en_US
dc.description.note © 2021 Society of Photo-Optical Instrumentation Engineers (SPIE). Due to copyright restrictions, the attached PDF file only contains the abstract of the full-text item. For access to the full-text item, please consult the publisher's website: https://doi.org/10.1117/1.JRS.15.024510 en_US
dc.description.cluster Advanced Agriculture & Food en_US
dc.description.impactarea Precision Agriculture en_US
dc.identifier.apacitation Adeyemi, A., Ramoelo, A., Cho, M. A., & Masemola, C. (2021). Spectral index to improve the extraction of built-up area from WorldView-2 imagery. <i>Journal of Applied Remote Sensing, 5(2)</i>, http://hdl.handle.net/10204/12103 en_ZA
dc.identifier.chicagocitation Adeyemi, A, A Ramoelo, Moses A Cho, and C Masemola "Spectral index to improve the extraction of built-up area from WorldView-2 imagery." <i>Journal of Applied Remote Sensing, 5(2)</i> (2021) http://hdl.handle.net/10204/12103 en_ZA
dc.identifier.vancouvercitation Adeyemi A, Ramoelo A, Cho MA, Masemola C. Spectral index to improve the extraction of built-up area from WorldView-2 imagery. Journal of Applied Remote Sensing, 5(2). 2021; http://hdl.handle.net/10204/12103. en_ZA
dc.identifier.ris TY - Article AU - Adeyemi, A AU - Ramoelo, A AU - Cho, Moses A AU - Masemola, C AB - Globally, the unprecedented increase in population in many cities has led to rapid changes in urban landscape, which requires timely assessments and monitoring. Accurate determination of built-up information is vital for urban planning and environmental management. Often, the determination of the built-up area information has been dependent on field surveys, which is laborious and time-consuming. Remote sensing data are the only option for deriving spatially explicit and timely built-up area information. There are few spectral indices for built-up areas and often not accurate as they are specific to impervious material, age, colour, and thickness, especially using higher resolution images. The objective of this study is to test the utility of a new built-up extraction index (NBEI) using WorldView-2 (WV-2) to improve built-up material mapping irrespective of material type, age, and color. The new index was derived from spectral bands such as green, red edge, NIR1, and NIR2 bands that profoundly explain the variation in built-up areas on WV-2 image. The result showed that NBEI improves the extraction of built-up areas with high accuracy [area under the receiver operating characteristic curve,   (  AUROC  )    =    ∼  0.82] compared to the existing indices such as built-up area index (AUROC  =    ∼  0.73), built-up spectral index (AUROC  =    ∼  0.78), red edge/green index (AUROC  =    ∼  0.71) and WorldView-Built-up Index (WV-BI) (AUROC  =    ∼  0.67). The study demonstrated that the new built-up index could extract built-up areas using high-resolution images. The performance of NBEI could be attributed to the fact that it is not material-specific, and would be necessary for urban area mapping. DA - 2021-04 DB - ResearchSpace DP - CSIR J1 - Journal of Applied Remote Sensing, 5(2) KW - Built-up KW - Spectral indices KW - Very high resolution KW - WorldView-2 LK - https://researchspace.csir.co.za PY - 2021 SM - 1931-3195 T1 - Spectral index to improve the extraction of built-up area from WorldView-2 imagery TI - Spectral index to improve the extraction of built-up area from WorldView-2 imagery UR - http://hdl.handle.net/10204/12103 ER - en_ZA
dc.identifier.worklist 24892 en_US


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