An Object-Based Approach for Urban Land Cover Classification: Integrating LiDAR Height and Intensity Data
Digital surface models (DSMs) derived from light detection and ranging (LiDAR) data have been increasingly integrated with high-resolution multispectral satellite/aerial imagery for urban land cover classification. Fewer studies, however, have investigated the usefulness of LiDAR intensity in aid of...
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Veröffentlicht in: | IEEE geoscience and remote sensing letters 2013-07, Vol.10 (4), p.928-931 |
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description | Digital surface models (DSMs) derived from light detection and ranging (LiDAR) data have been increasingly integrated with high-resolution multispectral satellite/aerial imagery for urban land cover classification. Fewer studies, however, have investigated the usefulness of LiDAR intensity in aid of urban land cover classification, particularly in highly developed urban settings. In this letter, we use an object-based classification approach to investigate whether a combination of LiDAR height and intensity data can accurately map urban land cover. We further compare the approach to a method that uses multispectral imagery as the primary data source, but LiDAR DSM as ancillary data to aid in classification. The study site is a suburban area in Baltimore County, MD. The LiDAR data were acquired in March 2005, from which DSM and two intensity layers (first and last returns), with 1-m spatial resolution were generated, respectively. Four classes were included: 1) buildings; 2) pavement; 3) trees and shrubs; and 4) grass. Our results indicated that the object-based approach provided flexible and effective means to integrate LiDAR height and intensity data for urban land cover classification. A combination of the LiDAR height and intensity data proved to be effective for urban land cover classification. The overall accuracy of the classification was 90.7%, and the overall Kappa statistics equaled 0.872, with the user's and producer's accuracies ranging from 86.8% to 93.6%. The accuracy of the results were far better than those using multispectral imagery alone, and comparable to using DSM data in combination with high-resolution multispectral satellite/aerial imagery. |
doi_str_mv | 10.1109/LGRS.2013.2251453 |
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Fewer studies, however, have investigated the usefulness of LiDAR intensity in aid of urban land cover classification, particularly in highly developed urban settings. In this letter, we use an object-based classification approach to investigate whether a combination of LiDAR height and intensity data can accurately map urban land cover. We further compare the approach to a method that uses multispectral imagery as the primary data source, but LiDAR DSM as ancillary data to aid in classification. The study site is a suburban area in Baltimore County, MD. The LiDAR data were acquired in March 2005, from which DSM and two intensity layers (first and last returns), with 1-m spatial resolution were generated, respectively. Four classes were included: 1) buildings; 2) pavement; 3) trees and shrubs; and 4) grass. Our results indicated that the object-based approach provided flexible and effective means to integrate LiDAR height and intensity data for urban land cover classification. A combination of the LiDAR height and intensity data proved to be effective for urban land cover classification. The overall accuracy of the classification was 90.7%, and the overall Kappa statistics equaled 0.872, with the user's and producer's accuracies ranging from 86.8% to 93.6%. The accuracy of the results were far better than those using multispectral imagery alone, and comparable to using DSM data in combination with high-resolution multispectral satellite/aerial imagery.</description><identifier>ISSN: 1545-598X</identifier><identifier>EISSN: 1558-0571</identifier><identifier>DOI: 10.1109/LGRS.2013.2251453</identifier><identifier>CODEN: IGRSBY</identifier><language>eng</language><publisher>IEEE</publisher><subject>Baltimore ; Buildings ; high-resolution imagery ; Image color analysis ; Image segmentation ; intensity ; Laser radar ; light detection and ranging (LiDAR) ; normalized digital surface model (nDSM) ; object-based image analysis ; Shape ; Spatial resolution ; urban land cover classification ; Vegetation</subject><ispartof>IEEE geoscience and remote sensing letters, 2013-07, Vol.10 (4), p.928-931</ispartof><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c379t-cc1a7e145b9f95b6f5340ce0f62d4c5063b1ac8f62996b56fd2a7fe8f00c492e3</citedby><cites>FETCH-LOGICAL-c379t-cc1a7e145b9f95b6f5340ce0f62d4c5063b1ac8f62996b56fd2a7fe8f00c492e3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/6497495$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>315,782,786,798,27931,27932,54765</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6497495$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Zhou, Weiqi</creatorcontrib><title>An Object-Based Approach for Urban Land Cover Classification: Integrating LiDAR Height and Intensity Data</title><title>IEEE geoscience and remote sensing letters</title><addtitle>LGRS</addtitle><description>Digital surface models (DSMs) derived from light detection and ranging (LiDAR) data have been increasingly integrated with high-resolution multispectral satellite/aerial imagery for urban land cover classification. Fewer studies, however, have investigated the usefulness of LiDAR intensity in aid of urban land cover classification, particularly in highly developed urban settings. In this letter, we use an object-based classification approach to investigate whether a combination of LiDAR height and intensity data can accurately map urban land cover. We further compare the approach to a method that uses multispectral imagery as the primary data source, but LiDAR DSM as ancillary data to aid in classification. The study site is a suburban area in Baltimore County, MD. The LiDAR data were acquired in March 2005, from which DSM and two intensity layers (first and last returns), with 1-m spatial resolution were generated, respectively. Four classes were included: 1) buildings; 2) pavement; 3) trees and shrubs; and 4) grass. Our results indicated that the object-based approach provided flexible and effective means to integrate LiDAR height and intensity data for urban land cover classification. A combination of the LiDAR height and intensity data proved to be effective for urban land cover classification. The overall accuracy of the classification was 90.7%, and the overall Kappa statistics equaled 0.872, with the user's and producer's accuracies ranging from 86.8% to 93.6%. The accuracy of the results were far better than those using multispectral imagery alone, and comparable to using DSM data in combination with high-resolution multispectral satellite/aerial imagery.</description><subject>Baltimore</subject><subject>Buildings</subject><subject>high-resolution imagery</subject><subject>Image color analysis</subject><subject>Image segmentation</subject><subject>intensity</subject><subject>Laser radar</subject><subject>light detection and ranging (LiDAR)</subject><subject>normalized digital surface model (nDSM)</subject><subject>object-based image analysis</subject><subject>Shape</subject><subject>Spatial resolution</subject><subject>urban land cover classification</subject><subject>Vegetation</subject><issn>1545-598X</issn><issn>1558-0571</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kN1Kw0AQRhdRsFYfQLzZF0jd3yTrXUy1LQQK1YJ3YbOZbbfUpOwuQt_ehIpXMx8zZ2AOQo-UzCgl6rlabD5mjFA-Y0xSIfkVmlAp84TIjF6PvZCJVPnXLboL4UAIE3meTZArOrxuDmBi8qoDtLg4nXyvzR7b3uOtb3SHK921uOx_wOPyqENw1hkdXd-94FUXYeeH0O1w5ebFBi_B7fYRj8g47IKLZzzXUd-jG6uPAR7-6hRt398-y2VSrRersqgSwzMVE2OozmD4oFFWySa1kgtigNiUtcJIkvKGapMPUam0kaltmc4s5JYQIxQDPkX0ctf4PgQPtj559639uaakHl3Vo6t6dFX_uRqYpwvjAOB_PxUqE0ryX0N0Zfo</recordid><startdate>20130701</startdate><enddate>20130701</enddate><creator>Zhou, Weiqi</creator><general>IEEE</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>20130701</creationdate><title>An Object-Based Approach for Urban Land Cover Classification: Integrating LiDAR Height and Intensity Data</title><author>Zhou, Weiqi</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c379t-cc1a7e145b9f95b6f5340ce0f62d4c5063b1ac8f62996b56fd2a7fe8f00c492e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Baltimore</topic><topic>Buildings</topic><topic>high-resolution imagery</topic><topic>Image color analysis</topic><topic>Image segmentation</topic><topic>intensity</topic><topic>Laser radar</topic><topic>light detection and ranging (LiDAR)</topic><topic>normalized digital surface model (nDSM)</topic><topic>object-based image analysis</topic><topic>Shape</topic><topic>Spatial resolution</topic><topic>urban land cover classification</topic><topic>Vegetation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhou, Weiqi</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><jtitle>IEEE geoscience and remote sensing letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Zhou, Weiqi</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An Object-Based Approach for Urban Land Cover Classification: Integrating LiDAR Height and Intensity Data</atitle><jtitle>IEEE geoscience and remote sensing letters</jtitle><stitle>LGRS</stitle><date>2013-07-01</date><risdate>2013</risdate><volume>10</volume><issue>4</issue><spage>928</spage><epage>931</epage><pages>928-931</pages><issn>1545-598X</issn><eissn>1558-0571</eissn><coden>IGRSBY</coden><abstract>Digital surface models (DSMs) derived from light detection and ranging (LiDAR) data have been increasingly integrated with high-resolution multispectral satellite/aerial imagery for urban land cover classification. Fewer studies, however, have investigated the usefulness of LiDAR intensity in aid of urban land cover classification, particularly in highly developed urban settings. In this letter, we use an object-based classification approach to investigate whether a combination of LiDAR height and intensity data can accurately map urban land cover. We further compare the approach to a method that uses multispectral imagery as the primary data source, but LiDAR DSM as ancillary data to aid in classification. The study site is a suburban area in Baltimore County, MD. The LiDAR data were acquired in March 2005, from which DSM and two intensity layers (first and last returns), with 1-m spatial resolution were generated, respectively. Four classes were included: 1) buildings; 2) pavement; 3) trees and shrubs; and 4) grass. Our results indicated that the object-based approach provided flexible and effective means to integrate LiDAR height and intensity data for urban land cover classification. A combination of the LiDAR height and intensity data proved to be effective for urban land cover classification. The overall accuracy of the classification was 90.7%, and the overall Kappa statistics equaled 0.872, with the user's and producer's accuracies ranging from 86.8% to 93.6%. The accuracy of the results were far better than those using multispectral imagery alone, and comparable to using DSM data in combination with high-resolution multispectral satellite/aerial imagery.</abstract><pub>IEEE</pub><doi>10.1109/LGRS.2013.2251453</doi><tpages>4</tpages></addata></record> |
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subjects | Baltimore Buildings high-resolution imagery Image color analysis Image segmentation intensity Laser radar light detection and ranging (LiDAR) normalized digital surface model (nDSM) object-based image analysis Shape Spatial resolution urban land cover classification Vegetation |
title | An Object-Based Approach for Urban Land Cover Classification: Integrating LiDAR Height and Intensity Data |
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