GRUČENJE PODATKOV LiDAR

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Title:	GRUČENJE PODATKOV LiDAR
Authors:	ID Založnik, Boštjan (Author) ID Žalik, Borut (Mentor) More about this mentor...
Files:	UNI_Zaloznik_Bostjan_2014.pdf (5,21 MB) MD5: 263658EBF569C5B5D3E46E4FF469E8FE
Language:	Slovenian
Work type:	Undergraduate thesis
Typology:	2.11 - Undergraduate Thesis
Organization:	FERI - Faculty of Electrical Engineering and Computer Science
Abstract:	Cilje diplomske naloge je raziskati možnosti uporabe algoritmov gručenja za obdelavo podatkov LiDAR. Prvi del diplomske naloge predstavlja podatke LiDAR in algoritme gručenja. S senzorji LiDAR, pritrjenimi na letala ali helikopterje, je omogočeno hitro in natančno modeliranje površja. Metode gručenje predstavljajo enega izmed pristopov za detekcijo objektov na površju. Gručenje je vrsta nenadzorovane klasifikacije podatkov. Za učinkovito implementacijo algoritmov gručenja so potrebne posebne podatkovne strukture. Pri diplomski nalogi uporabljamo drevesa KD. V drugem delu diplomske naloge predstavimo aplikacijo. Pripravi podatkov s predobdelavo sledi uporaba algoritma DBSCAN za iskanje zgradb in cest. Za iskanje dreves smo uporabili algoritem k-means. V zaključku podamo rezultate in uporabljene parametre.
Keywords:	algoritmi gručenja, k-means, DBSCAN, iskanje zgradb, iskanje cest, iskanje drevesnih krošenj, podatki LiDAR
Place of publishing:	Maribor
Publisher:	[B. Založnik]
Year of publishing:	2014
PID:	20.500.12556/DKUM-44193
UDC:	004.92.021(043.2)
COBISS.SI-ID:	17872150
NUK URN:	URN:SI:UM:DK:DPQZBTSQ
Publication date in DKUM:	20.05.2014
Views:	1793
Downloads:	198
Metadata:
Categories:	KTFMB - FERI
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Secondary language

Language:	English
Title:	CLUSTERING OF LiDAR DATA
Abstract:	The aim of this thesis is to explore clustering algorithms of point clouds collected by aerial LiDAR technology. The first part consideres LiDAR data and clustering algorithms. With LiDAR sensors that are mounted on air-planes or helicopters, fast and accurate surface modelling is possible. Clustering methods are one of the approaches for the detection of above-ground objects. Clustering is unsupervised classification of observed data into groups. For efficient implementation of clustering algorithms spatial data structures are required. KD-trees were used in this work. In the second part of diploma work, the developed application is presented. At first, the data was adequaetly preprocessed, then DBSCAN algorithm was used for buildings detection and road extraction. For tree crown delineation, the k-means algorithm was considered. The work is concluded with the results of clustering on real LiDAR data.
Keywords:	clustering algorithms, k-means, DBSCAN, building detection, road extraction, tree crown delineation, LiDAR data

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