@MastersThesis{Martins:2004:ClTeIm,
author = "Martins, Silvio Pimentel",
title = "Classifica{\c{c}}{\~a}o textural de imagens RADARSAT-1 para
discrimina{\c{c}}{\~a}o de alvos agr{\'{\i}}colas",
school = "Instituto Nacional de Pesquisas Espaciais (INPE)",
year = "2004",
address = "S{\~a}o Jos{\'e} dos Campos",
month = "2004-03-05",
keywords = "sensoriamento remoto, classifica{\c{c}}{\~a}o digital,
Radarsat-1, soja, cana-de-a{\c{c}}{\'u}car, texturas, remote
sensing, image classification, soybeans, sugar cane, textures.",
abstract = "As imagens de sensoriamento remoto da faixa do vis{\'{\i}}vel e
infravermelho do espectro eletromagn{\'e}tico apresentam grande
potencial na identifica{\c{c}}{\~a}o e discrimina{\c{c}}{\~a}o
de {\'a}reas agr{\'{\i}}colas para fins de estimativa de safra.
Contudo, a presen{\c{c}}a de nuvens impede a
aquisi{\c{c}}{\~a}o deste tipo de imagens. J{\'a} as imagens
obtidas na faixa espectral de microondas por radares imageadores
de abertura sint{\'e}tica independem de condi{\c{c}}{\~o}es
meteorol{\'o}gicas. Neste contexto, este trabalho tem por
objetivo verificar o potencial de imagens de radar na
identifica{\c{c}}{\~a}o das culturas de soja e
cana-de-a{\c{c}}{\'u}car na regi{\~a}o de Assis-SP,
atrav{\'e}s de classifica{\c{c}}{\~a}o textural. As imagens
utilizadas foram do RADARSAT-1/SAR C-HH nos seguintes modos de
aquisi{\c{c}}{\~a}o: Fine-5/descendente (F5D) de 30 de janeiro
de 2003; Fine-5/ascendente (F5A) de 14 de fevereiro de 2003; e
Standard-7/descendente (S7D) de 23 de fevereiro de 2003.
Adicionalmente foram utilizadas duas imagens do sistema Landsat-7
ETM+ adquiridas em 23 de fevereiro e 27 de mar{\c{c}}o de 2003
para servirem como refer{\^e}ncia na identifica{\c{c}}{\~a}o
dos alvos de interesse na {\'a}rea de estudo. Os m{\'e}todos
para identifica{\c{c}}{\~a}o das culturas foram baseados em
an{\'a}lises visuais e classifica{\c{c}}{\~o}es digitais
utilizando medidas de textura dentro das seguintes etapas: a)
defini{\c{c}}{\~a}o das classes de uso do solo; b) coleta das
amostras de treinamento e teste; c) gera{\c{c}}{\~a}o das bandas
de textura; d) classifica{\c{c}}{\~a}o supervisionada; e)
avalia{\c{c}}{\~a}o das classifica{\c{c}}{\~o}es atrav{\'e}s
da matriz de confus{\~a}o e do coeficiente kappa. As
classifica{\c{c}}{\~o}es digitais foram realizadas sobre as
imagens originais, filtradas e de textura atrav{\'e}s do
classificador pontual/contextual (MAXVER/ICM). Os resultados
indicaram que as classifica{\c{c}}{\~o}es realizadas sobre as
imagens filtradas e de textura foram, em geral, satisfat{\'o}rios
indicando que as medidas texturais podem ser ferramentas
{\'u}teis para maximizar a discrimina{\c{c}}{\~a}o de classes
de interesse em regi{\~o}es agr{\'{\i}}colas. ABSTRACT: Remote
sensing images from the visible and infrared regions of the
electromagnetic spectrum have demonstrated a great potential to
identify and discriminate agricultural areas for crops estimation.
However, cloud cover is an obstruction for this type of image
acquisition. On the other hand, Synthetic Aperture Radar (SAR)
images acquired in the microwave region of the electromagnetic
spectrum are independent of weather conditions. In this context,
this work has the objective to verify the capability of radar
images to identify soybean and sugarcane crops in the region of
Assis, S{\~a}o Paulo State using textural classification. Images
from RADARSAT-1/SAR C-HH were acquired in the following modes:
Fine-5/descending (F5D) from 31 January 2003; Fine-5/ascending
(F5A) from 14 February 2003; and Standard-7/descending (S7D) from
23 February 2003. Additionally, two cloud free Landsat-7 images
from 23 February and 27 March 2003 were used to identify targets
of interest in the study area. The methods for crops type
identification were based on visual and digital classification
analysis by using texture measures in the following steps: a)
definition of land use classes; b) extraction of training and test
samples; c) generation of texture bands; d) supervised
classification; and e) classification evaluations using confusion
matrix and kappa coefficient. Digital classifications using
MAXVER/ICM were carried out for: original, filtered and texture
images. The results indicated a good classification performance
for both filtered and texture images showing that the textural
measures can be a useful tool to maximize crop type
discrimination.",
committee = "Shimabukuro, Yosio Edemir (presidente) and Rudorff, Bernardo
Friederich Theodor (orientador) and Epiphanio, Jos{\'e} Carlos
Neves and Paradella, Waldir Renato and Sano, Edson Eyji",
copyholder = "SID/SCD",
englishtitle = "Agricultural targets discrimination by textural classification of
RADARSAT-1 imagery.",
language = "pt",
pages = "120",
ibi = "6qtX3pFwXQZ3P8SECKy/BLenQ",
url = "http://urlib.net/ibi/6qtX3pFwXQZ3P8SECKy/BLenQ",
targetfile = "publicacao.pdf",
urlaccessdate = "05 maio 2024"
}