@Article{KörtingFonsCastNami:2014:ImSaSe,
author = "K{\"o}rting, Thales Sehn and Fonseca, Leila Maria Garcia and
Castejon, Emiliano Ferreira and Namikawa, Laercio Massaru",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas
Espaciais (INPE)}",
title = "Improvements in Sample Selection Methods for Image
Classification",
journal = "Remote Sensing",
year = "2014",
volume = "6",
number = "8",
pages = "7580--7591",
keywords = "image classification, sample selection, remote sensing, Graphical
User Interface (GUI).",
abstract = "Traditional image classification algorithms are mainly divided
into unsupervised and supervised paradigms. In the first paradigm,
algorithms are designed to automatically estimate the classes
distributions in the feature space. The second paradigm depends on
the knowledge of a domain expert to identify representative
examples from the image to be used for estimating the
classification model. Recent improvements in human-computer
interaction (HCI) enable the construction of more intuitive
graphic user interfaces (GUIs) to help users obtain desired
results. In remote sensing image classification, GUIs still need
advancements. In this work, we describe our efforts to develop an
improved GUI for selecting the representative samples needed to
estimate the classification model. The idea is to identify changes
in the common strategies for sample selection to create a
user-driven sample selection, which focuses on different views of
each sample, and to help domain experts identify explicit
classification rules, which is a well-established technique in
geographic object-based image analysis (GEOBIA). We also propose
the use of the well-known nearest neighbor algorithm to identify
similar samples and accelerate the classification.",
doi = "10.3390/rs6087580",
url = "http://dx.doi.org/10.3390/rs6087580",
issn = "2072-4292",
label = "lattes: 8609036872819243 1 K{\"o}rtingFonsCastNami:2014:ImSaSe",
language = "en",
targetfile = "remotesensing-06-07580thales.pdf",
urlaccessdate = "27 abr. 2024"
}