@Article{SouzaKux:2014:GeMiDa,
author = "Souza, Ulisses Denache Vieira and Kux, Hermann Johann Heinrich",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)}",
title = "Geobia e minera{\c{c}}{\~a}o de dados na
classifica{\c{c}}{\~a}o da cobertura do solo urbano em S{\~a}o
Luis (MA) com imagens WorldView-2 e o sistema Interimage",
journal = "RBC: Revista Brasileira de Cartografia",
year = "2014",
volume = "66",
number = "3",
pages = "433--450",
month = "maio/jun.",
note = "Setores de Atividade: Administra{\c{c}}{\~a}o p{\'u}blica,
defesa e seguridade social. and Informa{\c{c}}{\~o}es
Adicionais: Abstract: Urban areas are characteristic spaces under
dynamic changes. Such areas are especially fragile when they are
located in coastal regions with mangrove vegetation and dune
ecosystems. Data processing of WorldView-2 satellite systems
considers the GEOBIA paradigm. In this study, the following
multispectral data from this satellite were used: bands red, green
and blue in the visible spectrum and a near infrared band. The
objective of this study was to evaluate the capability of these
datasets for the classification of land use/land cover in an urban
coastal area. Two test sites were considered at the northern
section of S{\~a}o Luis city (Maranh{\~a}o State, Brazil).
Initially tests were made with a classification model, considering
only those tools implemented at the InterIMAGE software package
(Test A1 and B1). For comparison purposes, a model was developed,
based on the results of data mining by decision tree, with a
minimum number of leaves, which indicates the best thresholds and
atributes for image classification. This model was adapted to the
concept of the InterIMAGE software (Test A11 and B11). After a
statistical evaluation, those classifications with highest Kappa
indices were considered, namely: test A11 (0,8354) and B11
(0,8446). So it was possible to customize the atributes validated
earlier at the land cover classification to the model used to map
land use, obtaining Kappa indicesof 0,7924 for {\'a}rea A and
0,7631 for {\'a}rea B..",
keywords = "WorldView-2, Minera{\c{c}}{\~a}o de dados, GEOBIA - Geographic
Object-based Image Analysis, Manguezais, Dunas, S{\~a}o Luis
(MA).",
abstract = "As {\'a}reas urbanas caracterizam-se por ser um espa{\c{c}}o em
transforma{\c{c}}{\~a}o. Quando est{\~a}o localizadas em
ambientes costeiros, tornam-se ainda mais fr{\'a}geis pela
presen{\c{c}}a de ecossistemas como os manguezais e as dunas.
Para o processamento e a avalia{\c{c}}{\~a}o de dados dos novos
sensores orbitais utiliza-se o paradigma de GEOBIA. Neste trabalho
foram usadas imagens do sat{\'e}lite WorldView-II de alta
resolu{\c{c}}{\~a}o espacial com 0,50m de resolu{\c{c}}{\~a}o
e oito bandas multiespectrais. O objetivo deste estudo foi avaliar
a capacidade do uso dessas imagens aliadas a t{\'e}cnicas de
minera{\c{c}}{\~a}o de dados, para a classifica{\c{c}}{\~a}o
da cobertura do solo urbano em {\'a}reas urbanas costeiras. Os
testes foram realizados em duas {\'a}reas-piloto no setor norte
da cidade de S{\~a}o Lu{\'{\i}}s - MA (Ilha do Maranh{\~a}o).
Inicialmente foram realizados testes com um modelo de
classifica{\c{c}}{\~a}o para as {\'a}reas-piloto, considerando
somente uma an{\'a}lise explorat{\'o}ria a partir das
ferramentas implementadas no software InterIMAGE (Teste AI e BI).
Para efeito de compara{\c{c}}{\~a}o, foi elaborado um modelo de
conhecimento que, com base nos resultados da minera{\c{c}}{\~a}o
de dados por {\'a}rvore de decis{\~a}o com um n{\'u}mero
m{\'{\i}}nimo de folhas, indicava os melhores limiares e
atributos para classifi car as imagens. Este modelo foi adaptado a
concep{\c{c}}{\~a}o do software InterIMAGE (Teste AII e BII).
Atrav{\'e}s de avalia{\c{c}}{\~o}es estat{\'{\i}}sticas foi
poss{\'{\i}}vel optar pelas classifica{\c{c}}{\~o}es com maior
precis{\~a}o que obtiveram {\'{\i}}ndices Kappa de 0,8354
(teste AII) e 0,8446 (teste BII). Desta forma foi
poss{\'{\i}}vel customizar os atributos anteriormente validados
na classifica{\c{c}}{\~a}o de cobertura da terra, obtendo-se
{\'{\i}}ndices Kappade 0,7924 para {\'a}rea A e 0,7631 para
{\'a}rea B. ABSTRACT: Urban areas are characteristic spaces under
dynamic changes. Such areas are especially fragile when they are
located in coastal regions with mangrove vegetation and dune
ecosystems. Data processing of the new high resolution remote
sensing satellite systems considers the GEOBIA paradigm. In this
study, the following multispectral data from the WorldView-II
satellite were used: bands Red, Green and Blue in the visible
spectrum and a near infrared band. The objective of this study was
to evaluate the capability of these datasets for the classifi
cation of land use/land cover in urban coastal areas. Two
test-sites were considered at the northern section of S{\~a}o
Lu{\'{\i}}s city (Maranh{\~a}o State, Brazil). Initially tests
were made with a classifi cation model, considering only those
tools implemented at the InterIMAGE classification software (Tests
AI and BI). For comparison purposes a model was developed, based
on the results of data mining by decision tree, with a minimum
number of leaves, which indicates the best thresholds and
attributes for image classification. This model was adapted to the
concept of the InterIMAGE software (Tests AII and BII). After a
statistical evaluation, those classifi cations with highest Kappa
indices were considered, namely: test AII (0.8354) and BII
(0.8446). So it was possible to customize the attributes validated
earlier at the land cover classifi cation to the model used to map
land use, obtaining Kappa indices of 0.7924 for area A and 0.7631
for area B.",
issn = "1808-0936",
label = "lattes: 3233696672067020 2 SouzaKux:2014:GeMiDa",
language = "pt",
url = "http://www.lsie.unb.br/rbc/index.php/rbc/article/view/913/691",
urlaccessdate = "06 maio 2024"
}