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Im, Jungho
Intelligent Remote sensing and geospatial Information Science (IRIS) Lab
Research Interests
  • Remote sensing, Artificial Intelligence, Geospatial modeling, Disaster monitoring and management, Climate change

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Object-based land cover classification using high-posting-density LiDAR data

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dc.contributor.author Im, Jungho ko
dc.contributor.author Jensen, John R. ko
dc.contributor.author Hodgson, Michael E. ko
dc.date.available 2014-11-05T02:05:47Z -
dc.date.created 2014-11-05 ko
dc.date.issued 2008-04 -
dc.identifier.citation GISCIENCE & REMOTE SENSING, v.45, no.2, pp.209 - 228 ko
dc.identifier.issn 1548-1603 ko
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/8289 -
dc.identifier.uri http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=44649087029 ko
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%. ko
dc.description.statementofresponsibility close -
dc.language ENG ko
dc.publisher BELLWETHER PUBL LTD ko
dc.subject CORRELATION IMAGE-ANALYSIS ko
dc.subject SPECIES COMPOSITION ko
dc.subject AERIAL-PHOTOGRAPHY ko
dc.subject AIRBORNE LIDAR ko
dc.subject LEAF-OFF ko
dc.subject ACCURACY ko
dc.subject TEXTURE ko
dc.title Object-based land cover classification using high-posting-density LiDAR data ko
dc.type ARTICLE ko
dc.identifier.scopusid 2-s2.0-44649087029 ko
dc.identifier.wosid 000255472300005 ko
dc.type.rims ART ko
dc.description.wostc 25 *
dc.description.scopustc 22 *
dc.date.tcdate 2015-05-06 *
dc.date.scptcdate 2014-11-05 *
dc.identifier.doi 10.2747/1548-1603.45.2.209 ko
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