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|Title: ||Selection of imagery data and classifiers for mapping Brazilian semideciduous Atlantic forests|
|???metadata.dc.creator???: ||Carvalho, Luis Marcelo Tavares de|
Clevers, Jan G.P.W.
Skidmore, Andrew K.
Jong, Steven M. de
|Keywords: ||Forest classification|
|Issue Date: ||2004|
|Citation: ||CARVALHO, L. M. T. et al. Selection of imagery data and classifiers for mapping Brazilian semideciduous Atlantic forests. International Journal of Applied Earth Observation and Geoinformation, Enschede, v. 5, n. 3, p. 173-186, Sept. 2004.|
|Abstract: ||This paper presents a case study on the use of features derived from remote sensing data for mapping the highly fragmented
semideciduous Atlantic forest in Brazil. Innovative aspects of this research include the evaluation of different feature sets in
order to improve land covermapping. The feature sets were defined based on expert knowledge and on data mining techniques
to be input to traditional and machine learning algorithms for pattern recognition, viz. maximum likelihood, univariate decision
trees, multivariate decision trees, and neural networks. The results showed that the maximum likelihood classification using
temporal texture descriptors as extracted with wavelet transforms was most accurate to classify the semideciduous Atlantic
forest. In this study, a special accuracy measure was used: the so-called class mapping accuracy. Maximum likelihood
performed relatively well, with forest mapping accuracies ranging from 34.5 to 51.3%. In contrast, accuracies for neural
networks ranged from 19.0 to 45.2%. Classification confusion occurred mainly with coffee and eucalyptus plantations.
Univariate trees provided the most robust results for different feature sets, with accuracies ranging from 39.6 to 46.7%.
Temporal information of vegetation indices was more important than image texture, terrain topography and raw spectral
information for discriminating semideciduous Atlantic forest.|
|Appears in Collections:||DCF - Artigos publicados em periódicos|
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