@InProceedings{DallaquaFariFaze:2018:AcLeAp,
author = "Dallaqua, Fernanda B. J. R. and Faria, Fabio A. and Fazenda,
Alvaro L.",
affiliation = "UNIFESP and UNIFESP and UNIFESP",
title = "Active Learning Approaches for Deforested Area Classification",
booktitle = "Proceedings...",
year = "2018",
editor = "Ross, Arun and Gastal, Eduardo S. L. and Jorge, Joaquim A. and
Queiroz, Ricardo L. de and Minetto, Rodrigo and Sarkar, Sudeep and
Papa, Jo{\~a}o Paulo and Oliveira, Manuel M. and Arbel{\'a}ez,
Pablo and Mery, Domingo and Oliveira, Maria Cristina Ferreira de
and Spina, Thiago Vallin and Mendes, Caroline Mazetto and Costa,
Henrique S{\'e}rgio Gutierrez and Mejail, Marta Estela and Geus,
Klaus de and Scheer, Sergio",
organization = "Conference on Graphics, Patterns and Images, 31. (SIBGRAPI)",
publisher = "IEEE Computer Society",
address = "Los Alamitos",
keywords = "Forest Monitoring, Active Learning, Remote Sensing Imagery.",
abstract = "The conservation of tropical forests is a social and ecological
relevant subject because of its important role in the global
ecosystem. Forest monitoring is mostly done by extraction and
analysis of remote sensing imagery (RSI) information. In the
literature many works have been successful in remote sensing image
classification through the use of machine learning techniques.
Generally, traditional learning algorithms demand a representative
and huge training set which can be an expensive procedure,
especially in RSI, where the imagery spectrum varies along seasons
and forest coverage. A semi-supervised learning paradigm known as
active learning (AL) is proposed to solve this problem, as it
builds efficient training sets through iterative improvement of
the model performance. In the construction process of training
sets, unlabeled samples are evaluated by a user-defined heuristic,
ranked and then the most relevant samples are labeled by an expert
user. In this work two different AL approaches (Confidence
Heuristics and Committee) are presented to classify remote sensing
imagery. In the experiments, our AL approaches achieve excellent
effectiveness results compared with well-known approaches existing
in the literature for two different datasets.",
conference-location = "Foz do Igua{\c{c}}u, PR, Brazil",
conference-year = "29 Oct.-1 Nov. 2018",
doi = "10.1109/SIBGRAPI.2018.00013",
url = "http://dx.doi.org/10.1109/SIBGRAPI.2018.00013",
language = "en",
ibi = "8JMKD3MGPAW/3RP9FF2",
url = "http://urlib.net/ibi/8JMKD3MGPAW/3RP9FF2",
targetfile = "sibgrapiID116.pdf",
urlaccessdate = "2024, May 02"
}