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

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

Cited 25 times inthomson ciCited 22 times inthomson ci
Title
Object-based land cover classification using high-posting-density LiDAR data
Author
Im, JunghoJensen, John R.Hodgson, Michael E.
Keywords
CORRELATION IMAGE-ANALYSIS; SPECIES COMPOSITION; AERIAL-PHOTOGRAPHY; AIRBORNE LIDAR; LEAF-OFF; ACCURACY; TEXTURE
Issue Date
2008-04
Publisher
BELLWETHER PUBL LTD
Citation
GISCIENCE & REMOTE SENSING, v.45, no.2, pp.209 - 228
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%.
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DOI
10.2747/1548-1603.45.2.209
ISSN
1548-1603
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UEE_Journal Papers
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