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Title:Algoritem za klasifikacijo točk vegetacije iz posnetkov LiDAR
Authors:ID Horvat, Denis (Author)
ID Žalik, Borut (Mentor) More about this mentor... New window
Files:.pdf DOK_Horvat_Denis_2017.pdf (11,60 MB)
MD5: 1F93D2CE1D5B7F27CDAB7AF48E7CE2CF
 
Language:Slovenian
Work type:Doctoral dissertation
Typology:2.08 - Doctoral Dissertation
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:V doktorski disertaciji predstavimo nov algoritem za klasifikacijo vegetacije v podatkih LiDAR. Klasifikacijski postopek povzamemo z dvema korakoma: analiza porazdelitve točk ter analiza njihovega konteksta. Poglavitna značilnost točk vegetacije je namreč statistično visoka razpršenost višin, zato jih lahko učinkovito razpoznamo v odvisnosti od napak pri prileganju ravnin. Klasifikacijo dodatno izboljšamo z uvedbo treh kontekstnih filtrov, ki obravnavajo povezane objekte (na primer zidove, dimnike, balkone), razraščeno vegetacijo in majhne objekte (na primer avtomobile, ograje, kipe). Pokazali smo, da lahko s predlaganim algoritmom vegetacijo razpoznamo neodvisno od tipa vegetacije (listavci in iglavci), okolja (gorsko, gozdnato, urbano) in nivoja olistanosti. V postopku validacije algoritma smo namreč v povrečju dosegli 97,9% rezultat F1 v neurbanih območjih in 91% v urbanih, ki iz vidika težavnosti klasifikacije veljajo za zahtevnejša. Pri klasifikaciji uporabljamo samo geometrijske podatke oblaka točk, kar predstavlja prednost algoritma pred drugimi, katerih uspešnost je v veliki meri odvisna od lastnosti, kot so visoka gostota točk in zanesljivost (ali prisotnost) drugih informacij. Analiza treh vhodnih parametrov je prav tako pokazala, da so le-ti stabilni in robustni. Predlagani algoritem zato omogoča uporabniško interakcijo ter nadzor razmerja celovitosti in pravilnosti klasifikacije.
Keywords:algoritem, daljinsko zaznavanje, LiDAR, matematična morfologija, prileganje površij, klasifikacija vegetacije
Place of publishing:Maribor
Publisher:[D. Horvat]
Year of publishing:2017
PID:20.500.12556/DKUM-65091 New window
UDC:004.652.6:5514.04(043.3)
COBISS.SI-ID:20461590 New window
NUK URN:URN:SI:UM:DK:YU9WY3XY
Publication date in DKUM:04.04.2017
Views:1993
Downloads:352
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
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Secondary language

Language:English
Title:Algorithm for classification of vegetation in LiDAR data
Abstract:In this Doctoral dissertation, we introduce a new algorithm for the classification of vegetation points within LiDAR data. The classification procedure can be outlined with two steps: An analysis of the distribution of points and an analysis of the context in which they are located. Vegetation points are, namely, characterised by their non-linear distributions and they can, therefore, be recognised effciently in relation to the large plane fitting errors. The classification is improved further by introducing three contextual filters, which deal with attached objects (e.g. walls, chimneys, balconies), overgrown vegetation and small objects (e.g. vehicles, fences, statues). We have shown that the proposed algorithm is able to classify vegetation despite of the different vegetation types (deciduous and coniferous), enviroments (mountain, forested,urban), and different levels of leaf conditions. An average F1 score of 97.9% was achieved for non-urban areas and 91% for urban areas, which are, in terms of classification difficulty, considered to be more difficult. The algorithm uses only the geometrical information of points, which presents an advantage when compared with the methods which rely on point clouds with high point density and reliability (or the presence) of other information. The parameter sensitivity analysis has also shown that the three used input parameters are stable and robust. They, therefore, provide an efficient way to regulate the ratio between completeness and correctness.
Keywords:algorithm, remote sensing, LiDAR, mathematical morphology, surface fitting, vegetation classification


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