@Article{ReisDutrSantEsca:2017:ExMuCh,
author = "Reis, Mariane Sousa and Dutra, Luciano Vieira and Sant'Anna,
Sidnei Jo{\~a}o Siqueira and Escada, Maria Isabel Sobral",
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 = "Examining multi-legend change detection in amazon with pixel and
region based methods",
journal = "Remote Sensing",
year = "2017",
volume = "9",
number = "1",
keywords = "Amazon, Change detection, Multi-legend, Pixel based
classification, Region based classification.",
abstract = "Post-classification comparison is one of the most widely used
change detection methods. However, it presents several operational
problems that are often ignored, such as the occurrence of
impossible transitions, difficulties in accuracy assessment and
results not accurate enough for the purpose. This work aims to
evaluate post-classification comparison change detection results
obtained from LANDSAT5/TM data in a region of the Brazilian
Amazon, using three legends in different levels of detail and both
pixel wise and region based classifiers. A distinctive
characteristic of the used approach is that each change mapping is
the result of the combination of 100 land cover classifications
for each date, obtained using varied training samples. This
approach allowed to account for the training samples choice into
the methodology, as well as the construction of confidence
mappings. We presented and discussed different approaches for
evaluating change results, such as the likelihood of land cover
transitions occurring within the study area and time gap, the use
of rectangular matrices to incorporate the occurrence of
impossible or non evaluable changes and classification
uncertainty. In general, change mappings obtained from region
based classifications showed better results than the ones obtained
from pixel based classifications. Globally, the use of region
based approaches, in contrast to pixel based ones, led to an
increase in accuracy of 15.5% for the change mapping from the most
detailed legend, 7.8% for the one with the legend with
intermediate level of detail and 3.6% for the less detailed one.
In addition, individual transitions between land cover classes
were better identified using region based approaches, with the
exception of transitions from a non agriculture class to an
agricultural one. The proposed quality mappings are useful to help
to evaluate the change mappings, mainly in legend levels with
higher level of detail and if reference samples are unreliable or
unavailable. It was possible to access, in a spatially explicit
way, that at least 29.0% of the pixel based change mapping and
21.9% of the region based one from the most detailed legend were
erroneous classified, without ground truth information on the
evaluated date. These values decreased to 0.5% and 1.4%
(respectively the pixel and region based approaches) for results
with the legend with the intermediate level of detail and are non
existent in the results from the less detailed legend. The more
generalized the legend (lower number of classes), the most similar
are the accuracy of region and pixel based change mappings. These
accuracy values also increase as fewer classes are considered in
the legend, since similar classes are assembled during clustering,
which reduces the overlap between groups. However, this accuracy
is still low for operational purposes in areas with few changes,
even considering the very high accuracy of the land cover
classifications used to generate the change mappings (land cover
classification with Overall Accuracy higher than 0.98 resulted in
change mappings with Overall Accuracy around 0.83).",
doi = "10.3390/rs9010077",
url = "http://dx.doi.org/10.3390/rs9010077",
issn = "2072-4292",
language = "en",
targetfile = "reis_examining.pdf",
urlaccessdate = "26 abr. 2024"
}