Waveform-based point cloud classification in land-cover identification

Yi Hsing Tseng, Cheng Kai Wang, Hone Jay Chu, Yu Chia Hung

研究成果: Article

10 引文 斯高帕斯(Scopus)

摘要

Full-waveform topographic LiDAR data provide more detailed information about objects along the path of a laser pulse than discrete-return (echo) topographic LiDAR data. Full-waveform topographic LiDAR data consist of a succession of cross-section profiles of landscapes and each waveform can be decomposed into a sum of echoes. The echo number reveals critical information in classifying land cover types. Most land covers contain one echo, where as topographic LiDAR data in trees and roof edges contained multi-echo waveform features. To identify land-cover types, waveform-based classifier was integrated single-echoand multi-echo classifiers for point cloud classification. The experimental area was the Namasha district of Southern Taiwan, and the land-cover objects were categorized as roads, trees (canopy), grass (grass and crop), bare (bare ground), and buildings (buildings and roof edges). Waveform features were analyzed with respect to the single- and multi-echo laser-pathsamples, and the critical waveform features were selected according to the Bhattacharyya distance. Next, waveform-based classifiers were performed using support vector machine (SVM) with the local, spatial features of waveform topographic LiDAR information, and optical image information. Results showed that by using fused waveform and optical information, the waveform-based classifiers achieved the highest overall accuracy in identifying land-cover point clouds among the models, especially when compared toan echo-based classifier.

原文English
頁(從 - 到)78-88
頁數11
期刊International Journal of Applied Earth Observation and Geoinformation
34
發行號1
DOIs
出版狀態Published - 2015 一月 1

All Science Journal Classification (ASJC) codes

  • Global and Planetary Change
  • Earth-Surface Processes
  • Computers in Earth Sciences
  • Management, Monitoring, Policy and Law

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