@InCollection{SantosFerrPicoCâma:2019:SeMaEa,
author = "Santos, Lorena Alves and Ferreira, Karine Reis and Picoli,
Michelle Cristina Ara{\'u}jo and C{\^a}mara, Gilberto",
title = "Self-organizing maps in earth observation data cubes analysis",
booktitle = "Advances in self-organizing maps, learning vector quantization,
clustering and data visualization",
publisher = "Springer",
year = "2019",
editor = "Vellido, A. and Gibert, K. and Angulo, C. and Mart{\'{\i}}n
Guerrero, J. D.",
pages = "70--79",
keywords = "Self-Organizing Maps · Earth Observation Data Cubes Analysis ·
Satellite image time series · Land Use and Cover Changes.",
abstract = "Earth Observation (EO) Data Cubes infrastructures model
analysis-ready data generated from remote sensing images as
multidimensional cubes (space, time and properties), especially
for satellite image time series analysis. These infrastructures
take advantage of big data technologies and methods to store,
process and analyze the big amount of Earth observation satellite
images freely available nowadays. Recently, EO Data Cubes
infrastructures and satellite image time series analysis have
brought new opportunities and challenges for the Land Use and
Cover Change (LUCC) monitoring over large areas. LUCC have caused
a great impact on tropical ecosystems, increasing global
greenhouse gases emissions and reducing the planets biodiversity.
This paper presents the utility of Self-Organizing Maps (SOM)
neural network method in the process to extract LUCC information
from EO Data Cubes infrastructures, using image time series
analysis. Most classification techniques to create LUCC maps from
satellite image time series are based on supervised learning
methods. In this context, SOM is used as a method to assess land
use and cover samples and to evaluate which spectral bands and
vegetation indexes are best suitable for the separability of land
use and cover classes. A case study is described in this work and
shows the potential of SOM in this application.",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas
Espaciais (INPE)}",
doi = "10.1007/978-3-030-19642-4",
url = "http://dx.doi.org/10.1007/978-3-030-19642-4",
isbn = "978-3-030-19642-4",
language = "en",
targetfile = "santos_self.pdf",
urlaccessdate = "16 jun. 2024"
}