@Article{ZanottaBruzBovoShim:2015:AdSeAp,
author = "Zanotta, Daniel Capella and Bruzzone, Lorenzo and Bovolo,
Francesca and Shimabukuro, Yosio Edemir",
affiliation = "National Institute for Science, Education and Technology, Rio
Grande, Brazil and Department of Information Engineering and
Computer Science, University of Trento, Trento, Italy and Center
for Information and Communication Technology, Fondazione Bruno
Kessler, Trento, Italy and {Instituto Nacional de Pesquisas
Espaciais (INPE)}",
title = "An adaptive semisupervised approach to the detection of
user-defined recurrent changes in image time series",
journal = "IEEE Transactions on Geoscience and Remote Sensing",
year = "2015",
volume = "53",
number = "7",
pages = "3707--3719",
month = "Jul.",
keywords = "Change detection, deforestation, domain adaptation, forest fires,
recurrent change, time series.",
abstract = "In this paper, we present a novel domain adaptation technique
aimed at providing reliable change detection maps for a series of
image pairs acquired on the same area at different times. The
proposed technique exploits the polar change vector analysis
method and assumes that the reference data for characterizing a
specific change of interest are available only for a pair of
images (source domain). Then, it exploits the knowledge learned
from the source domain and adapts it to other pairs of images
belonging to the time series (target domains) to be analyzed. The
proposed technique is able to handle possible radiometric
differences among images adapting in an unsupervised way the
decision rule estimated on the source domain to the target domains
through variables estimated directly on the target images. The
proposed approach has been applied to two data sets made up of
time series of Landsat Thematic Mapper images. In one case, the
change of interest is related to evolution of deforestation, while
in the other case, it is related to burned area detection.
Experimental results show the effectiveness of the proposed
technique.",
doi = "10.1109/TGRS.2014.2381645",
url = "http://dx.doi.org/10.1109/TGRS.2014.2381645",
issn = "0196-2892",
language = "en",
targetfile = "zanotta_adaptive.pdf",
urlaccessdate = "27 abr. 2024"
}