@Article{PinhoFonKörAlmKux:2012:LaClIn,
author = "Pinho, Carolina Moutinho Duque and Fonseca, Leila Maria Garcia and
K{\"o}rting, Thales Sehn and Almeida, Cl{\'a}udia Maria de and
Kux, Hermann Johann Heinrich",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas
Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais
(INPE)}",
title = "Land-cover classification of an intra-urban environment using
high-resolution images and object-based image analysis",
journal = "International Journal of Remote Sensing",
year = "2012",
volume = "33",
number = "19",
pages = "5973--5995",
month = "Oct.",
note = "Informa{\c{c}}{\~o}es Adicionais: Detailed, up-to-date
information on intra-urban land cover is important for urban
planning and management. Differentiation between permeable and
impermeable land, for instance, provides data for surface run-off
and flood prevention, whereas identification of vegetated areas
enables studies of urban micro-climates. In place of maps,
high-resolution images, such as those from IKONOS II, Quickbird-2,
OrbView and WorldView-2, can be used after processing.
Object-based image analysis (OBIA) is a well-established method
for classifying high-resolution images of urban areas. Despite the
large number of previous studies of OBIA in the context of
intra-urban analysis, there are many issues in this area that are
still open to discussion and solution. Intra-urban analysis using
OBIA can be lengthy and complex because of the processing
difficulties related to image segmentation, the large number of
object attributes to be resolved and the many different methods
needed to classify various image objects. To overcome these
issues, we performed an experiment consisting of land cover
mapping based on an OBIA approach, using an IKONOS II image of a
southern sector of S{\~a}o Jos{\'e} dos Campos city (covering an
area of 12 km2 with 50 neighbourhoods), which is located in
S{\~a}o Paulo State, in SE Brazil. This area contains various
occupation and land-use patterns, and it therefore contains a wide
range of intra-urban targets. To generate the land-cover map, we
proposed an OBIA-based processing framework that combines
multi-resolution segmentation, data mining and hierarchical
network techniques. The intra-urban land-cover map was then
evaluated through an object-based error matrix, and classification
accuracy indices were obtained. The final classification, with 11
classes, achieved a global accuracy of 71.91%..",
keywords = "An{\'a}lise de imagens orientada a objeto - OBIA,
Classifica{\c{c}}{\~a}o de imagens baseada em conhecimento,
IKONOS, QUICKBIRD, Planejamento Urbano, S{\~a}o Jos{\'e} dos
Campos-SP.",
abstract = "Detailed, up-to-date information on intra-urban land cover is
important for urban planning and management. Differentiation
between permeable and impermeable land, for instance, provides
data for surface run-off estimates and flood prevention, whereas
identification of vegetated areas enables studies of urban
micro-climates. In place of maps, high-resolution images, such as
those from the satellites IKONOS II, Quickbird, Orbview and
WorldView II, can be used after processing. Object-based image
analysis (OBIA) is a well-established method for classifying
high-resolution images of urban areas. Despite the large number of
previous studies of OBIA in the context of intra-urban analysis,
there are many issues in this area that are still open to
discussion and resolution. Intra-urban analysis using OBIA can be
lengthy and complex because of the processing difficulties related
to image segmentation, the large number of object attributes to be
resolved and the many different methods needed to classify various
image objects. To overcome these issues, we performed an
experiment consisting of land-cover mapping based on an OBIA
approach using an IKONOS II image of a southern sector of S{\~a}o
Jos{\'e} dos Campos city (covering an area of 12 km2 with 50
neighbourhoods), which is located in S{\~a}o Paulo State in
south-eastern Brazil. This area contains various occupation and
land-use patterns, and it therefore contains a wide range of
intra-urban targets. To generate the land-cover map, we proposed
an OBIA-based processing framework that combines multi-resolution
segmentation, data mining and hierarchical network techniques. The
intra-urban land-cover map was then evaluated through an
object-based error matrix, and classification accuracy indices
were obtained. The final classification, with 11 classes, achieved
a global accuracy of 71.91%.",
doi = "10.1080/01431161.2012.675451",
url = "http://dx.doi.org/10.1080/01431161.2012.675451",
issn = "0143-1161",
label = "lattes: 3233696672067020 5 PinhoFonKorAlmKux:2012:LaClIn",
language = "en",
targetfile = "Pinho_CMD.pdf",
urlaccessdate = "11 maio 2024"
}