@InProceedings{ArabiFernPiza:2015:HMHySp,
author = "Arabi, Samir Youssif Wehbi and Fernandes, David and Pizarro, Marco
Ant{\^o}nio",
affiliation = "Instituto Federal de Educa{\c{c}}{\~a}o, Ci{\^e}ncia e
Tecnologia de Go{\'{\i}}as (IFG) and {Instituto Tecnol{\'o}gico
de Aeron{\'a}utica (ITA)} and {Instituto Nacional de Pesquisas
Espaciais (INPE)}",
title = "HMM for hyperspectral spectrum representation and classification
with endmember entropy vectors",
booktitle = "Proceedings...",
year = "2015",
organization = "Image and Signal Processing for Remote Sensing, 21.",
keywords = "Hyperspectral, image classification, HMM, entropy.",
abstract = "The Hyperspectral images due to its good spectral resolution are
extensively used for classification, but its high number of bands
requires a higher bandwidth in the transmission data, a higher
data storage capability and a higher computational capability in
processing systems. This work presents a new methodology for
hyperspectral data classification that can work with a reduced
number of spectral bands and achieve good results, comparable with
processing methods that require all hyperspectral bands. The
proposed method for hyperspectral spectra classification is based
on the Hidden Markov Model (HMM) associated to each Endmember (EM)
of a scene and the conditional probabilities of each EM belongs to
each other EM. The EM conditional probability is transformed in EM
vector entropy and those vectors are used as reference vectors for
the classes in the scene. The conditional probability of a
spectrum that will be classified is also transformed in a spectrum
entropy vector, which is classified in a given class by the
minimum ED (Euclidian Distance) among it and the EM entropy
vectors. The methodology was tested with good results using AVIRIS
spectra of a scene with 13 EM considering the full 209 bands and
the reduced spectral bands of 128, 64 and 32. For the test area
its show that can be used only 32 spectral bands instead of the
original 209 bands, without significant loss in the classification
process.",
conference-location = "Toulouse, France",
conference-year = "21 Sept.",
doi = "10.1117/12.2194135",
url = "http://dx.doi.org/10.1117/12.2194135",
targetfile = "96430P.pdf",
urlaccessdate = "09 maio 2024"
}