Improving Classification Accuracy of Multi-Temporal Landsat Images by Assessing the Use of Different Algorithms, Textural and Ancillary Information for a Mediterranean Semiarid Area from 2000 to 2015
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2017-10-17Resumen
The aim of this study was to evaluate three different strategies to improve classification accuracy in a highly fragmented semiarid area using, (i) different classification algorithms with parameter optimization in some cases; (ii) different feature sets including spectral, textural and terrain features; and (iii) different seasonal combinations of images. A three-way ANOVA was used to discern which of these approaches and their interactions significantly increases accuracy. Tukey–Kramer contrast using a heteroscedasticity-consistent estimation of the kappa covariances matrix was used to check for significant differences in accuracy. The experiment was carried out with Landsat TM, ETM and OLI images corresponding to the period 2000–2015. A combination of four images using random forest and the three feature sets was the best way to improve accuracy. Maximum likelihood, random forest and support vector machines do not significantly increase accuracy when textural information was added, ...
Palabra/s clave
land use classification
machine learning
textural information
contextual information