@InProceedings{ArabiFernPizaPinh:2015:TySeCl,
author = "Arabi, Samir and Fernandes, David and Pizarro, Marco Ant{\^o}nio
and Pinho, Marcelo",
affiliation = "Instituto Federal de Educa{\c{c}}{\~a}o, Ci{\^e}ncia e
Tecnologia de Go{\'{\i}}as and {Instituto Tecnol{\'o}gico de
Aeron{\'a}utica (ITA)} and {Instituto Nacional de Pesquisas
Espaciais (INPE)} and {Instituto Tecnol{\'o}gico de
Aeron{\'a}utica (ITA)}",
title = "Typical sequence classification method in hyperspectral images
with reduced bands",
booktitle = "Proceedings...",
year = "2015",
organization = "IEEE International Geoscience and Remote Sensing Symposium
(IGARSS)",
keywords = "Typical Sequence, Hyperspectral, Image Classification, HMM.",
abstract = "This work presents a new method for hyperspectral spectra
classification based on the Typical Sequence (TS) derived from the
Asymptotic Equipartition Theorem and Information Theory. Each
Endmember (EM) of a scene is represented by a Hidden Markov Model
(HMM) and a spectrum is classified in a given class if it can be
considered a TS generated by the HMM associated with the EM
related to the class. The Discrete Wavelet Transform (DWT) is used
in the orthogonal decomposition of the original spectrum and the
HMM parameters are estimated using this orthogonal decomposition.
The proposed method is tested with AVIRIS spectra of a scene with
13 EM and the classification results show that 32 spectral bands
can be used instead of the original 209 bands, without significant
loss in the classification process.",
conference-location = "Milan, Italy",
conference-year = "23-31 July",
urlaccessdate = "28 abr. 2024"
}