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Multi-Scale Hierarchical Conditional Random Field for Railway Electrification Scene Classification Using Mobile Laser Scanning Data

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dc.contributor.advisorSohn, Gunho
dc.creatorChen, Leihan
dc.date.accessioned2018-11-21T13:47:24Z
dc.date.available2018-11-21T13:47:24Z
dc.date.copyright2018-06-25
dc.date.issued2018-11-21
dc.date.updated2018-11-21T13:47:24Z
dc.degree.disciplineEarth & Space Science
dc.degree.levelMaster's
dc.degree.nameMSc - Master of Science
dc.description.abstractWith the recent rapid development of high-speed railway in many countries, precise inspection for railway electrification systems has become more significant to ensure safe railway operation. However, this time-consuming manual inspection is not satisfactory for the high-demanding inspection task, thus a safe, fast and automatic inspection method is required. With LiDAR (Light Detection and Ranging) data becoming more available, the accurate railway electrification scene understanding using LiDAR data becomes feasible towards automatic 3D precise inspection. This thesis presents a supervised learning method to classify railway electrification objects from Mobile Laser Scanning (MLS) data. First, a multi-range Conditional Random Field (CRF), which characterizes not only labeling homogeneity at a short range, but also the layout compatibility between different objects at a middle range in the probabilistic graphical model is implemented and tested. Then, this multi-range CRF model will be extended and improved into a hierarchical CRF model to consider multi-scale layout compatibility at full range. The proposed method is evaluated on a dataset collected in Korea with complex railway electrification systems environment. The experiment shows the effectiveness of proposed model.
dc.identifier.urihttp://hdl.handle.net/10315/35526
dc.language.isoen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectComputer science
dc.subject.keywordsRailway electrification system
dc.subject.keywordsConditional random field
dc.subject.keywordsSpatial regularities
dc.subject.keywordsMobile laser scanning
dc.subject.keywordsPoint cloud classification
dc.titleMulti-Scale Hierarchical Conditional Random Field for Railway Electrification Scene Classification Using Mobile Laser Scanning Data
dc.typeElectronic Thesis or Dissertation

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