@InProceedings{DomicianoDiasShigSilv:2017:EsAuMo,
author = "Domiciano, Marco Antonio Pizani and Dias, Luiz Alberto Vieira and
Shiguemori, Elcio Hideiti and Silva Filho, Paulo Fernando
Ferreira",
title = "Estima{\c{c}}{\~a}o Autom{\'a}tica do Modelo Digital de
Eleva{\c{c}}{\~a}o a partir de Imagens A{\'e}rea",
booktitle = "Anais...",
year = "2017",
editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz
Eduardo Oliveira e Cruz de",
pages = "7891--7898",
organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 18. (SBSR)",
publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
address = "S{\~a}o Jos{\'e} dos Campos",
abstract = "The development and employment of UAV have grown in last years.
With this, theapplications have also been increase. Among the
applications, an important process is to automaticestimation of
the Digital Elevation Model for a specific area. In many cases, it
is important that the flightbe made autonomously. The successful
autonomous aerial navigation is dependent on several factors,among
them the location of the unmanned aerial vehicle into the
geographical space. In order to achievethis goal, there exist
techniques that combine the Satellite Position System (SPS) with
the InertialNavigation System (INS). However, these techniques
have been dependent on external data (signal fromthe SPS), that
could not be available during the vehicle operation. When the SPS
and the INS are notavailable, Computational Vision is an
alternative for the navigation. Some studies have been
developedabout navigation with the use of images and Digital
Elevation Models. In this paper it is presented anapproach to
automatically estimate a Digital Elevation Model using aerial
images. Different techniqueshave been used to obtain
characteristic points, from two images obtained at different
instants. TheZernike Moment and Particle Collision Algorithm are
used to find correspondence between the aerialimages. The results
show that the proposed techniques have been suitable for this
problem.",
conference-location = "Santos",
conference-year = "28-31 maio 2017",
isbn = "978-85-17-00088-1",
label = "59991",
language = "pt",
organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
ibi = "8JMKD3MGP6W34M/3PSMGK2",
url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PSMGK2",
targetfile = "59991.pdf",
type = "Cartografia e fotogrametria",
urlaccessdate = "06 maio 2024"
}