@Article{ReisDutrEscaSant:2020:AvInTr,
author = "Reis, Mariane Souza and Dutra, Luciano Vieira and Escada, Maria
Isabel Sobral and Sant'Anna, Sidnei Jo{\~a}o Siqueira",
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)}",
title = "Avoiding invalid transitions in land cover trajectory
classification with a compound maximum a posteriori approach",
journal = "IEEE Access",
year = "2020",
volume = "8",
pages = "98787--98799",
month = "June",
keywords = "Classification algorithms, land cover trajectory mapping,
multi-temporal classification, remote sensing, remote
monitoring.",
abstract = "Classifying remote sensing time-series in land cover trajectories
can provide essential information about ecosystems functioning and
about the impacts of natural phenomena or human activities over
the environment. The existing approaches commonly used for this
task can present serious drawbacks, such as the possibility to
derive invalid trajectories, use of complex data inputs, and
several classification steps. To provide a simple method for land
cover trajectory classification, we present a novel algorithm
named Compound Maximum a Posteriori (CMAP) classifier. CMAP
incorporates the knowledge of land cover dynamics and the
information of multi-temporal data sets to produce only valid land
cover trajectories using a global generative classification
approach and simple inputs. CMAP was tested in two case studies,
in which we compared land cover trajectories obtained by CMAP to
those obtained using the traditional Maximum Likelihood (ML)
classifier in a post-classification comparison approach. In the
first case study, we classified 6 images from the same sensor,
using the same land cover legend. In the second case study, we
classified 3 images from different types of sensors, using
different land cover legend levels. Because of its formulation,
CMAP does not return invalid transitions/trajectories as
classification results. The use of ML and post-classification
comparison, on the other hand, resulted in invalid land cover
trajectories in more than 50% of the used images in both case
studies. Furthermore, the use of CMAP leads to better accuracy
indexes for land cover classification of each date and reduces the
classification noise.",
doi = "10.1109/ACCESS.2020.2997019",
url = "http://dx.doi.org/10.1109/ACCESS.2020.2997019",
issn = "2169-3536",
language = "en",
targetfile = "reis_avoiding.pdf",
urlaccessdate = "18 abr. 2024"
}