@Article{SoaresKörtFonsBend:2020:SiNoIt,
author = "Soares, Anderson Reis and K{\"o}rting, Thales Sehn and Fonseca,
Leila Maria Garcia and Bendini, Hugo do Nascimento",
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 = "Simple nonlinear iterative temporal clustering",
journal = "IEEE Transactions on Geoscience and Remote Sensing",
year = "2020",
volume = "1",
number = "1",
pages = "1--11",
abstract = "Classifying dense satellite image time series has become a
necessity, especially with the recent efforts to create analysis
ready data cubes. Approaches developed to perform this task are
usually pixel-based. Even though these approaches can achieve good
results, they do not take advantage of the intrinsic spatial
correlation of geographic data nor do they consider spatial
heterogeneity along with the time series. Region-based
classification is a suitable solution to incorporate contextual
information for dense satellite image time series classification.
In this article, we introduce a new segmentation method based on a
superpixel approach. This method creates multitemporal
superpixels, which are meaningful regions in space and time. To
evaluate the performance of the proposed method, tests were
performed on two data sets using a total of 23 ground-truth
references. Experimental results showed that the method performed
well, achieving a good boundary agreement and obtaining high
scores on the three metrics used for evaluation.",
doi = "10.1109/TGRS.2020.3033266",
url = "http://dx.doi.org/10.1109/TGRS.2020.3033266",
issn = "0196-2892",
label = "lattes: 5123287769635741 3 SoaresK{\"o}rtFonsBend:2020:SiNoIt",
language = "pt",
targetfile = "soares_simple.pdf",
urlaccessdate = "26 maio 2024"
}