@InProceedings{DutraNegrSantLu:2015:CaStNe,
author = "Dutra, Luciano Vieira and Negri, Rog{\'e}rio Galante and
Sant'Anna, Sidnei Jo{\~a}o Siqueira and Lu, Dengsheng",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)}",
title = "Development of dissimilarity functions using stochastic distances
for region-based land cover classification: a case study near
Tapaj{\'o}s Flona, Par{\'a} state, Brazil",
booktitle = "Anais...",
year = "2015",
editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz
Eduardo Oliveira e Cruz de",
pages = "1655--1662",
organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 17. (SBSR)",
publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
address = "S{\~a}o Jos{\'e} dos Campos",
abstract = "One recent alternative to standard pixel based classification of
remote sensing data, is the region based classification (RBC),
which has been proved particularly useful when analyzing high
resolution imagery of complex environments, like urban areas.
First the imagery is decomposed into homogenous regions, following
some criteria, and then each region is classified to one of the
classes of interest. Normally, classification is performed by
using stochastic distances, which measures the distance of the
pixels distribution inside an unknown region and the
representative distributions of each class. The class, whose
distance is minimum to the unknown region distribution, is
assigned to the region, which is known as stochastic minimum
distance classification (SMDC). A problem appears when one, or
more, class distribution is multi-modal, which violates the
Gaussian hypotheses used for classes distributions, degrading the
mapping accuracy. This investigation reports the usage of
different compositions of the original stochastic minimum distance
classifier with the objective of getting less sensitive results
for classification, when potentially multi-modal classes are used.
The newly developed classifier, called stochastic nearest distance
classifier (SNDC), produced the best result when compared with the
original classifier and other possible compositions, in a study
case near the Tapaj{\'o}s Flona, in Par{\'a} state, Brazil. This
study also brings, as methodological contribution, a criterion to
improve the segmentation phase of RBC methods.",
conference-location = "Jo{\~a}o Pessoa",
conference-year = "25-29 abr. 2015",
isbn = "978-85-17-0076-8",
label = "309",
language = "en",
organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
ibi = "8JMKD3MGP6W34M/3JM4973",
url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3JM4973",
targetfile = "p0309.pdf",
type = "Classifica{\c{c}}{\~a}o e minera{\c{c}}{\~a}o de dados",
urlaccessdate = "2024, Apr. 27"
}