@Article{MorenoCorLenGraGal:2014:ExLeMa,
author = "Moreno, Ram{\'o}n and Corona, Francesco and Lendasse, Amaury and
Graņa, Manuel and Galv{\~a}o, L{\^e}nio Soares",
affiliation = "{Universidad del Pa{\'{\i}}s Vasco} and {Aalto University} and
{Aalto University} and {Universidad del Pa{\'{\i}}s Vasco} and
{Instituto Nacional de Pesquisas Espaciais (INPE)}",
title = "Extreme learning machines for soybean classification in remote
sensing hyperspectral images",
journal = "Neurocomputing",
year = "2014",
volume = "128",
pages = "207--216",
keywords = "Agricultural remote sensing, Classification performance,
Classification process, Extreme learning machine, Feed-forward
network, Functional data analysis, Hyper-spectral images,
State-of-the-art algorithms, Algorithms, Crops, Knowledge
acquisition, Maps, Network layers, Pixels, Remote sensing,
Spectroscopy, Classification (of information), accuracy,
agricultural land, algorithm, article, Brazil, classification,
crop, extreme learning machine, image analysis, linear system,
machine learning, mathematical computing, mathematical model,
nonlinear system, priority journal, remote sensing, signal noise
ratio, soybean.",
abstract = "This paper focuses on the application of Extreme Learning Machines
(ELM) to the classification of remote sensing hyperspectral data.
The specific aim of the work is to obtain accurate thematic maps
of soybean crops, which have proven to be difficult to identify by
automated procedures. The classification process carried out is as
follows: First, spectral data is transformed into a
hyper-spherical representation. Second, a robust image gradient is
computed over the hyper-spherical representation allowing an image
segmentation that identifies major crop plots. Third, feature
selection is achieved by a greedy wrapper approach. Finally, a
classifier is trained and tested on the selected image pixel
features. The classifiers used for feature selection and final
classification are Single Layer Feedforward Networks (SLFN)
trained with either the ELM or the incremental OP-ELM. Original
image pixel features are computed following a Functional Data
Analysis (FDA) characterization of the spectral data. Conventional
ELM training of the SLFN improves over the classification
performance of state of the art algorithms reported in the
literature dealing with the data treated in this paper. Moreover,
SLFN-ELM uses less features than the referred algorithms. OP-ELM
is able to find competitive results using the FDA features from a
single spectral band.",
doi = "10.1016/j.neucom.2013.03.057",
url = "http://dx.doi.org/10.1016/j.neucom.2013.03.057",
issn = "0925-2312 and 1872-8286",
label = "scopus 2014-05 MorenoCorLenGraGal:2014:ExLeMa",
language = "en",
targetfile = "1-s2.0-S0925231213010102-main.pdf",
urlaccessdate = "11 maio 2024"
}