@InProceedings{SchultzImmiFormAtzb:2015:ObCrCl,
author = "Schultz, Bruno and Immitzer, Markus and Formaggio, Ant{\^o}nio
Roberto and Atzberger, Clement",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {} and
{Instituto Nacional de Pesquisas Espaciais (INPE)}",
title = "Object-based crop classification using multitemporal OLI imagery
and Chain Classification with Random Forest",
booktitle = "Anais...",
year = "2015",
editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz
Eduardo Oliveira e Cruz de",
pages = "3059--3066",
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 = "The use of more than one Landsat-like data sensor to automatically
classify different crops is still a challenge. Improvements have
been made using different images to map crops for large areas. The
chain classification (CC) has permitted the use of samples in the
overlapping area (between two Landsat-like images) to classify
cultures at regional scale with an automatic classification. The
Random Forest (RF) model is an automatic ensemble learning
classifier with possible feature selection. RF can also provide
reliability measures of the classification results for each
segment. The goal of this work was to analyze the sugarcane
classification in South of S{\~a}o Paulo State, using
object-based approach, multitemporal images, random forest and
chain classification. In the first step the images from
August/2013 and January/2014 (221/76 and 222/76) were segmented
and reference samples were manually selected from MCC (medium
cycle crop), SCC (short cycle crop), LCC (long cycle crop), Water
body (WB) and others (OT) to generate the first RF model
(M1=overlapping). In the Second step we extracted the samples with
high majority difference from the RFM1 model. After that, the best
samples were used to classify each image in the second model
(M2=221/76) and third model (M3=222/76). The obtained overall
accuracies (OA) were 77.2 % (221/76) and 73.4 % (222/76). The
results could may be improved if the samples were selected from
low and high majority difference values.",
conference-location = "Jo{\~a}o Pessoa",
conference-year = "25-29 abr. 2015",
isbn = "978-85-17-0076-8",
label = "608",
language = "en",
organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
ibi = "8JMKD3MGP6W34M/3JM4ALT",
url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3JM4ALT",
targetfile = "p0608.pdf",
type = "Classifica{\c{c}}{\~a}o e minera{\c{c}}{\~a}o de dados",
urlaccessdate = "28 abr. 2024"
}