Title page for etd-0204110-193203


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URN etd-0204110-193203
Author Tsung-shao Tsai
Author's Email Address onion67@gmail.com
Statistics This thesis had been viewed 5537 times. Download 1061 times.
Department Marine Environment and Engineering
Year 2009
Semester 1
Degree Master
Type of Document
Language zh-TW.Big5 Chinese
Title The Study of Knowledge-Based Lidar Data Filtering and Terrain Recovery
Date of Defense 2010-01-20
Page Count 89
Keyword
  • light detection and ranging (LiDAR)
  • digital elevation model (DEM)
  • knowledge-based LiDAR filtering (KBLF)
  • Abstract There is an increasing need for three-dimensional description for various applications such as the development of catchment areas, forest fire control and restoration. Three-dimensional information plays an indispensable role; therefore acquisition of the digital elevation models (DEMs) is the first step in these applications.
         LiDAR is a recent development in remote sensing with great potential for providing high resolution and accurate three-dimensional point clouds for describing terrain surface. The acquired LiDAR data represents the surface where the laser pulse is reflected from the height of the terrain and object above ground. These objects should be removed to derive the DEMs. Many LiDAR data-filtering studies are based on surface, block, and slope algorithms. These methods have been developed to filter out most features above the terrain; however, in certain situations they have proved unsatisfactory.
    The different algorithm based on different point of view to describe the terrain surface. The appropriate adoption of the advantages from these algorithms will develop a more complete way to derive DEMs. Knowledge-based system is developed to solve some specific problems according to the given appropriate domain knowledge. Huang (2007) proposed a Knowledge-based classification system in urban feature classification using LiDAR data and high resolution aerial imagery with 93% classification accuracy. This research proposed a knowledge-based LiDAR filtering (KBLF) as a follow-up study of Huang’s study. KBLF integrates various knowledge rules derived from experts in the area of ground feature extraction using LiDAR data to increase the capability of describing terrain and ground feature classification. The filtering capability of KBLF is enhanced as expected to get better quality of referenced ground points to recover terrain height and DEMs using Inverse Distance Weighting (IDW) and Nearest Neighbor (NN) methods.
    Advisory Committee
  • Tian-Yuan Shih - chair
  • Yi-Hsing Tseng - co-chair
  • Liang-Hwei Lee - co-chair
  • Shiahn-wern Shyue - advisor
  • Ming-Jer Huang - advisor
  • Files
  • etd-0204110-193203.pdf
  • indicate access worldwide
    Date of Submission 2010-02-04

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