@InProceedings{SantosMuKuGaKuBrPa:2010:ClTeIm,
author = "Santos, Joao Roberto dos and Mura, Jos{\'e} Claudio and Kux,
Herman Johann Heinrich and Garcia, Cesar Edwin and Kuntz, Steffen
and Brown, Irving Foster and Pantoja, Nara Vidigal",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)} and {} and {Infoterra GmbH Germany}
and {Woods Hole Research Center United States} and Institute for
the Environment and Renewable Natural Resources - IBAMA/Acre,
Brazil",
title = "Classification of TerraSAR-X Imagery for the Characterization of
Amazon Tropical Forests",
booktitle = "Proceedings...",
year = "2010",
organization = "30th European Association of Remote Sensing Laboratories - EARSeL
Symposium.",
note = "Setores de Atividade: Produ{\c{c}}{\~a}o Florestal, Pesquisa e
desenvolvimento cient{\'{\i}}fico.",
keywords = "monitoring, tropical forest, image processing, SAR data, land
cover.",
abstract = "The objective of this study is to analyze the potential of TERRA
SAR-X dual images, on the StripMap mode, for classification of
forest cover and of land use classes resulting from human
activities. The area under study is located in the portions of SW
Brazilian Amazon region, Acre State. The Single Look Complex
images of TERRA SAR-X (ascending mode, slant range and azimuth
resolution of 1m and 6m, respectively) in HH and VV polarizations,
were processed in accordance with the following methodological
steps: (a) generation of the covariance matrix (windows of 3x5
pixels); (b) application of the Enhanced Lee filter to reduce the
speckle noise; (c) targets decomposition technique based on the
Cloude and Pottier theorem; (d) thematic classification by
algorithm MLC + ICM (Maximum Likelihood Classifier + Iterated
Conditional Modes); (e) assessment of classification accuracy by
Kappa statistics. This approach has shown the potential of TERRA
SAR-X images for the discrimination of primary forest, degraded
forest, pastures and agricultural areas/bare soil. The best
classification performance was derived from the combination of the
amplitude image (resulting from covariance matrix) and the entropy
image generated from the decomposition of targets. The overall
classification accuracy was 76% and the Kappa value of 0.67, whose
analysis were supported by field survey realized simultaneously to
the acquisition of the radar images. The state of art of the
forest conditions analyzed by X-band radar imagery will be an
important tool, together with other frequencies/SAR systems, to
subsidize both the inventory and monitoring processes of land
use/land cover in Brazilian Amazon.",
conference-location = "Paris Paris",
conference-year = "2010",
label = "lattes: 1646956319628219 1 SantosMuKuGaKuBrPa:2010:ClTeIm",
language = "en",
targetfile = "santos classifcacao.doc",
urlaccessdate = "27 abr. 2024"
}