@Article{NegriDutrSant:2016:CoSuVe,
author = "Negri, Rog{\'e}rio G. and Dutra, Luciano Vieira and Sant'Anna,
Sidnei Jo{\~a}o Siqueira",
affiliation = "{Universidade Estadual Paulista (UNESP)} and {Instituto Nacional
de Pesquisas Espaciais (INPE)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)}",
title = "Comparing support vector machine contextual approaches for urban
area classification",
journal = "Remote Sensing Letters",
year = "2016",
volume = "7",
number = "5",
pages = "485--494",
month = "May",
abstract = "Support vector machine (SVM) has been receiving a great deal of
attention for remote sensing data classification. Although the
original formulation of this method does not incorporate
contextual information, lately different formulations have been
proposed to incorporate such information, with the aim of
improving the mapping accuracy. In general, these proposals modify
the SVM training phase or integrate the SVM classifications in
stochastic models. Recently, two new contextual versions of SVM,
context adaptive and competitive translative SVM (CaSVM and CtSVM,
respectively), were proposed in literature. In this work, two case
studies of urban area classification, using IKONOS-II and
hyperspectral digital imagery collection experiment (HYDICE) data
sets were conducted to compare SVM, SVM integrated with the
iterated conditional modes (ICM) stochastic algorithm, SVM
smoothed using the mode filter and the recent approaches CaSVM and
CtSVM. The results indicated that although it possesses a high
computational cost, the CaSVM method was able to produce
classification results with similar accuracy (using kappa
coefficient) to those obtained using SVM integrated with ICM
(SVM+ICM) and the mode filter (SVM+Mode), all of them found
statistically superior to the SVM result at 95% confidence level
for the IKONOS-II image. For HYDICE image, all results were found
statistically insignificant at 95% confidence level. Investigation
of what happens at transition regions between classes, however,
showed that some methods can present superior performance. To this
objective, a new performance measure, called upsilon coefficient,
was introduced in this work, which measures the impact that the
smoothing effect, typical of contextual methods, can have in
distorting the edges between regions. With this new measure was
found that CaSVM is the one which has better performance followed
with SVM+ICM.",
doi = "10.1080/2150704X.2016.1154218",
url = "http://dx.doi.org/10.1080/2150704X.2016.1154218",
issn = "2150-704X",
language = "en",
urlaccessdate = "05 jun. 2024"
}