@InProceedings{ZalotiJrGonFreSanSan:2006:EvPoSA,
author = "Zaloti Jr., Orlando D. and Gon{\c{c}}alves, F{\'a}bio G. and
Freitas, Corina da Costa and Sant’Anna, Sidnei Jo{\~a}o S. and
Santos, Jo{\~a}o Roberto dos",
affiliation = "Divis{\~a}o de Geo-Intelig{\^e}ncia. Instituto de Estudos
Avan{\c{c}}ados, IEAv, S{\~a}o Jos{\'e} dos Campos and
{Instituto Nacional de Pesquisas Espaciais. Divis{\~a}o de
Processamento de Imagens} and {nstituto Nacional de Pesquisas
Espaciais. Divis{\~a}o de Sensoriamento Remoto}",
title = "Evaluating the Potential of SAR-R99B L and X Bands Data for Amazon
Deforestation Increment Mapping",
booktitle = "Proceedings...",
year = "2006",
pages = "2662 - 2665",
organization = "Geoscience and Remote Sensing Symposium (IGARSS); Canadian
Symposium on Remote Sensing, 28.",
publisher = "IEEE",
address = "IEEE",
keywords = "Algorithms, Radar imaging, Synthetic aperture radar, Change
detection, Forest monitoring, Iterated Conditional Modes (ICM),
Tropical forest, Deforestation, Algorithms, Deforestation,
Radar.",
abstract = "The main objective of this paper is to evaluate the potential of
the SAR-R99B airborne radar images (L-band Quad-Pol and X-band HH)
from the Amazon Protection System (SIPAM) to discriminate the
increment of deforested areas. In order to achieve this purpose,
two classification approaches are considered. In the first
approach, the contextual algorithm Iterated Conditional Modes
(ICM) is used to classify only amplitude SAR images. The second
approach consists on synthesizing TM/Landsat fraction images from
several polarimetric SAR attributes, preserving the automated
procedures currently developed in the Amazon annual monitoring
activity throughout the Prodes Digital Project methodology.",
conference-location = "Denver",
conference-year = "31 July - 4 Aug.",
doi = "10.1109/IGARSS.2006.687",
url = "http://dx.doi.org/10.1109/IGARSS.2006.687",
isbn = "0780395107 and 9780780395107",
language = "en",
organisation = "IEEE",
targetfile = "04_14A04.pdf",
urlaccessdate = "21 maio 2024"
}