@Article{NegriDutrSant:2014:InSuVe,
author = "Negri, Rogerio Galante and Dutra, Luciano Vieira and Sant'Anna,
Sidnei Joao Siqueira",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)}",
title = "An innovative support vector machine based method for contextual
image classification",
journal = "ISPRS Journal of Photogrammetry and Remote Sensing",
year = "2014",
volume = "87",
pages = "241--248",
month = "Jan.",
keywords = "image classification, contextual information, support vector
machine.",
abstract = "Several remote sensing studies have adopted the Support Vector
Machine (SVM) method for image classification. Although the
original formulation of the SVM method does not incorporate
contextual information, there are different proposals to
incorporate this type of information into it. Usually, these
proposals modify the SVM training phase or make an integration of
SVM classifications using stochastic models. This study presents a
new perspective on the development of contextual SVMs. The main
concept of this proposed method is to use the contextual
information to displace the separation hyperplane, initially
defined by the traditional SVM. This displaced hyperplane could
cause a change of the class initially assigned to the pixel. To
evaluate the classification effectiveness of the proposed method a
case study is presented comparing the results with the standard
SVM and the SVM post-processed by the mode (majority) filter. An
ALOS/PALSAR image, PLR mode, acquired over an Amazon area was used
in the experiment. Considering the inner area of test sites, the
accuracy results obtained by the proposed method is better than
SVM and similar to SVM post-processed by the mode filter. The
proposed method, however, produces better results than mode
post-processed SVM when considering the classification near the
edges between regions. One drawback of the method is the
computational cost of the proposed method is significantly greater
than the compared methods.",
doi = "10.1016/j.isprsjprs.2013.11.004",
url = "http://dx.doi.org/10.1016/j.isprsjprs.2013.11.004",
issn = "0924-2716",
label = "isi 2014-05 NegriDutrSiqu:2014:InSuVe",
language = "en",
urlaccessdate = "02 maio 2024"
}