@InProceedings{NegriDutrSant:2012:StApMi,
author = "Negri, Rog{\'e}rio Galante and Dutra, Luciano Vieira and
Sant'Anna, Sidinei Jo{\~a}o Siqueira",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)}",
title = "Stochastic Approaches of Minimum Distance Method for Region Based
Classification",
booktitle = "Proceedings...",
year = "2012",
editor = "al, Alvarez et",
pages = "797--804",
organization = "Progress in Pattern Recognition, Image Analysis, Computer Vision,
and Applications;Iberoamerican Congress, 17. (CIARP).",
publisher = "Springer-Verlag",
note = "{Lecture Notes in Computer Science} and {Volume 7441 2012}",
keywords = "Computer vision, Image analysis, Image reconstruction, Remote
sensing, Stochastic systems, Classification process, Image
simulations, Minimum average distance, Minimum distance,
Region-based, Simple approach, Simulation studies, Stochastic
approach, stochastic distances.",
abstract = "Normally remote sensing image classification is performed
pixelwise which produces a noisy classification. One way of
improving such results is dividing the classification process in
two steps. First, uniform regions by some criterion are detected
and afterwards each unlabeled region is assigned to class of the
{"}nearest{"} class using a so-called stochastic distance. The
statistics are estimated by taking in account all the reference
pixels. Three variations are investigated. The first variation is
to assign to the unlabeled region a class that has the minimum
average distance between this region and each one of reference
samples of that class. The second is to assign the class of the
closest reference sample. The third is to assign the most frequent
class of the k closest reference regions. A simulation study is
done to assess the performances. The simulations suggested that
the most robust and simple approach is the second variation.",
conference-location = "Buenos Aires Berlin",
conference-year = "2012",
isbn = "16113349 and {13: 9783642332746}",
issn = "03029743",
label = "lattes: 8201805132981288 1 NegriDutrSant:2012:StApMi",
language = "en",
targetfile = "negri_stochastic.pdf",
url = "http://www.springerlink.com/content/kuv75681m5806613/",
volume = "7441",
urlaccessdate = "04 maio 2024"
}