@Article{ZanottaZortFerr:2018:SuApSi,
author = "Zanotta, Daniel Capella and Zortea, Maciel and Ferreira, Matheus
Pinheiro",
affiliation = "Instituto Nacional de Ci{\^e}ncia, Educa{\c{c}}{\~a}o e
Tecnologia and {IBM Research} and {Instituto Nacional de Pesquisas
Espaciais (INPE)}",
title = "A supervised approach for simultaneous segmentation and
classification of remote sensing images",
journal = "ISPRS Journal of Photogrammetry and Remote Sensing",
year = "2018",
volume = "142",
pages = "162--173",
month = "Aug.",
keywords = "Object-based image analysis, Segmentation, Supervised
classification, Multispectral imaging.",
abstract = "Object-based image classification is recognized as one of the best
strategies to analyze high spatial resolution remote sensing
images. This process includes defining scale parameters to form
regions sharing similar characteristics such as color, texture, or
shape. Traditionally, in an object-based supervised classification
setting the image is classified only after the segmentation
process is completed. However, when the imaged objects on the
ground are heterogeneous and of different sizes, some resulting
segments can be appropriate for classification while others are
over or under-segmented. This may cause partial failure of the
subsequent classification. In this paper, we introduce a
simultaneous approach based on the interception of the
segmentation stage by provisional classification of under-growing
segments. Our proposal is to optimize the classification process
by iteratively updating the labels of previously generated regions
only if the estimated posterior probabilities of the winning
classes in the new segments increase. Experiments with three
multispectral datasets acquired by Landsat-5 TM, QuickBird-II, and
WorldView-3 in rural and urban areas compare traditional
object-based approach based on region growing with the proposed
method using well-established classifiers. Our results show that
the proposed method becomes much less sensitive to the choice of
segmentation parameters and reaches similar, or even better,
classification accuracies.",
doi = "10.1016/j.isprsjprs.2018.05.021",
url = "http://dx.doi.org/10.1016/j.isprsjprs.2018.05.021",
issn = "0924-2716",
language = "en",
targetfile = "zanotta_supervised.pdf",
urlaccessdate = "27 abr. 2024"
}