@Article{Brum-BastosRibPinKörFon:2016:ImEvCe,
author = "Brum-Bastos, V. S. and Ribeiro, B. M. G. and Pinheiro, C. M. D.
and K{\"o}rting, Thales Sehn and Fonseca, Leila Maria Garcia",
affiliation = "{University of St. Andrews} and {Universidade Federal do Rio
Grande do Sul (UFRGS)} and {Universidade Federal do ABC (UFABC)}
and {Instituto Nacional de Pesquisas Espaciais (INPE)} and
{Instituto Nacional de Pesquisas Espaciais (INPE)}",
title = "Improvement evaluation on ceramic roof extraction using
WorldView-2 imagery and geographic data mining approach",
journal = "International Archives of the Photogrammetry, Remote Sensing and
Spatial Information Sciences",
year = "2016",
volume = "41",
pages = "883--889",
month = "July",
note = "3rd International Archives of the Photogrammetry, Remote Sensing
and Spatial Information Sciences Congress, ISPRS 2016; Prague;
Czech Republic; 12 -19 July 2016.",
keywords = "C4.5, Ceramic roof, Classification accuracy, Decision tree,
GEOBIA, Geographical data mining, WorldView-2.",
abstract = "Advances in geotechnologies and in remote sensing have improved
analysis of urban environments. The new sensors are increasingly
suited to urban studies, due to the enhancement in spatial,
spectral and radiometric resolutions. Urban environments present
high heterogeneity, which cannot be tackled using pixel-based
approaches on high resolution images. Geographic Object-Based
Image Analysis (GEOBIA) has been consolidated as a methodology for
urban land use and cover monitoring; however, classification of
high resolution images is still troublesome. This study aims to
assess the improvement on ceramic roof classification using
WorldView-2 images due to the increase of 4 new bands besides the
standard {"}Blue-Green-Red-Near Infrared{"} bands. Our methodology
combines GEOBIA, C4.5 classification tree algorithm, Monte Carlo
simulation and statistical tests for classification accuracy. Two
samples groups were considered: 1) eight multispectral and
panchromatic bands, and 2) four multispectral and panchromatic
bands, representing previous high-resolution sensors. The C4.5
algorithm generates a decision tree that can be used for
classification; smaller decision trees are closer to the semantic
networks produced by experts on GEOBIA, while bigger trees, are
not straightforward to implement manually, but are more accurate.
The choice for a big or small tree relies on the user's skills to
implement it. This study aims to determine for what kind of user
the addition of the 4 new bands might be beneficial: 1) the common
user (smaller trees) or 2) a more skilled user with coding and/or
data mining abilities (bigger trees). In overall the
classification was improved by the addition of the four new bands
for both types of users.",
doi = "10.5194/isprsarchives-XLI-B7-883-2016",
url = "http://dx.doi.org/10.5194/isprsarchives-XLI-B7-883-2016",
issn = "1682-1750",
language = "en",
urlaccessdate = "27 abr. 2024"
}