@InProceedings{PadilhaFerrEspiJúni:2017:MoÁrCu,
author = "Padilha, Thiago Kerr and Ferreira, Jo{\~a}o Ausgusto De Carvalho
and Espinoza, Jean Marcel de Almeida and J{\'u}nior, Adilson
Jos{\'e} Pereira",
title = "Monitoramento das {\'a}reas cultivadas de soja e arroz
atrav{\'e}s da classifica{\c{c}}{\~a}o de imagens orbitais",
booktitle = "Anais...",
year = "2017",
editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz
Eduardo Oliveira e Cruz de",
pages = "7930--7937",
organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 18. (SBSR)",
publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
address = "S{\~a}o Jos{\'e} dos Campos",
abstract = "This work aims at introduce a new cognitive classification method
based on color attributes. Implemented in a supervised manner, the
method assumes the user collect samples from unlimited classes in
a limited space of attributes (only three). Then, the samples
elements are converted to HSV space and plotted in a reduced HSV
colored diagram. The user has also to select polygons on this HSV
space in order to generalize the comprehensive space of each
class. Finally, the image is all converted to HSV space and each
element is considered in order to define if it lies in a region
occupied by one class in the HSV reduced space. The main advantage
of the proposed classification process is the power to emulate the
human knowledge used by photo interpreter during visual
interpretation of remote sensing image targets. Experiments
performed with an image marked by deforestation in Amazon were
conducted by comparing the performance of several classification
approaches. The method proved to be useful in noncomplex problems
when simple approaches tend to show adequate results. Other
advantages of the proposed method is its simplicity and
interactivity, besides it ability to generalize the sampling
process when taking homogeneous samples is a difficult task.",
conference-location = "Santos",
conference-year = "28-31 maio 2017",
isbn = "978-85-17-00088-1",
label = "60133",
language = "pt",
organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
ibi = "8JMKD3MGP6W34M/3PSMGLN",
url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PSMGLN",
targetfile = "60133.pdf",
type = "Monitoramento e modelagem ambiental",
urlaccessdate = "15 jun. 2024"
}