@Article{ArabaiFernPizaPinh:2016:HyImCl,
author = "Arabai, S. Youssif Wehbi and Fernandes, D. and Pizarro, Marco
Antonio and Pinho, M. da Silva",
affiliation = "Instituto Federal de Educa{\c{c}}{\~a}o, Ci{\^e}ncia e
Tecnologia de Goi{\'a}s (IFG) 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 = "Hyperspectral images classification with typical sequences
associated to the endmember",
journal = "IEEE Latin America Transactions",
year = "2016",
volume = "14",
number = "7",
pages = "3102--3109",
month = "July",
keywords = "Classification, HMM, Hyperspectral, Typical Sequences, Wavelet.",
abstract = "This paper presents a new methodology for hyperspectral image
classification based on the definition of typical sets from the
Asymptotic Equipartition Property, an important tool in the field
of information theory. The Endmembers (EM) are decomposed in
orthogonal functions by a discrete wavelet transform and are
modeled as a HMM (Hidden Markov Model). Based on this model, for
each EM, a Typical Sequence set is established. One spectrum is
classified as a member of a specific EM if belongs to its typical
set. It is considered the case in which a class in the
hyperspectral image can be represented by several subclasses and
also the original spectra can be decimated and be used with less
bands in the classification processes. The proposed method is
tested with a set of AVIRIS data and is compared with the
classification performed by Euclidian Distance, Spectral Angle
Mapper (SAM) and Spectral Information Divergence (SID). It is
shown that the proposed classification can be used with a reduced
number of bands and achieves results comparable with other methods
using all bands.",
doi = "10.1109/TLA.2016.7587608",
url = "http://dx.doi.org/10.1109/TLA.2016.7587608",
issn = "1548-0992",
language = "en",
targetfile = "arabi_hyperspectral.pdf",
urlaccessdate = "27 abr. 2024"
}