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Im, Jungho
Intelligent Remote sensing and geospatial Information Science Lab.
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dc.citation.endPage 228 -
dc.citation.number 2 -
dc.citation.startPage 209 -
dc.citation.title GISCIENCE & REMOTE SENSING -
dc.citation.volume 45 -
dc.contributor.author Im, Jungho -
dc.contributor.author Jensen, John R. -
dc.contributor.author Hodgson, Michael E. -
dc.date.accessioned 2023-12-22T08:41:51Z -
dc.date.available 2023-12-22T08:41:51Z -
dc.date.created 2014-11-05 -
dc.date.issued 2008-04 -
dc.description.abstract This study introduces a method for object-based land cover classification based solely on the analysis of LiDAR-derived information - i.e., without the use of conventional optical imagery such as aerial photography or multispectral imagery. The method focuses on the relative information content from height, intensity, and shape of features found in the scene. Eight object-based metrics were used to classify the terrain into land cover information: mean height, standard deviation (STDEV) of height, height homogeneity, height contrast, height entropy, height correlation, mean intensity, and compactness. Using machine-learning decision trees, these metrics yielded land cover classification accuracies > 90%. A sensitivity analysis found that mean intensity was the key metric for differentiating between the grass and road/parking lot classes. Mean height was also a contributing discriminator for distinguishing features with different height information, such as between the building and grass classes. The shape- or texture-based metrics did not significantly improve the land cover classifications. The most important three metrics (i.e., mean height, STDEV height, and mean intensity) were sufficient to achieve classification accuracies > 90%. -
dc.identifier.bibliographicCitation GISCIENCE & REMOTE SENSING, v.45, no.2, pp.209 - 228 -
dc.identifier.doi 10.2747/1548-1603.45.2.209 -
dc.identifier.issn 1548-1603 -
dc.identifier.scopusid 2-s2.0-44649087029 -
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/8289 -
dc.identifier.url http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=44649087029 -
dc.identifier.wosid 000255472300005 -
dc.language 영어 -
dc.publisher BELLWETHER PUBL LTD -
dc.title Object-based land cover classification using high-posting-density LiDAR data -
dc.type Article -
dc.description.journalRegisteredClass scie -
dc.description.journalRegisteredClass scopus -
dc.subject.keywordPlus CORRELATION IMAGE-ANALYSIS -
dc.subject.keywordPlus SPECIES COMPOSITION -
dc.subject.keywordPlus AERIAL-PHOTOGRAPHY -
dc.subject.keywordPlus AIRBORNE LIDAR -
dc.subject.keywordPlus LEAF-OFF -
dc.subject.keywordPlus ACCURACY -
dc.subject.keywordPlus TEXTURE -

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