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Open AccessArticle

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

1
Instituto Euromediterráneo del Agua, Campus de Espinardo, s/n, 30001 Murcia, Spain
2
Instituto Universitario del Agua y Medio Ambiente, Universidad de Murcia. Edificio D, Campus de Espinardo, s/n, 30001 Murcia, Spain
3
Unidad Predepartamental de Ingeniería Civil, Universidad Politécnica de Cartagena, Paseo Alfonso XIII, 52. 30203 Cartagena, Spain
4
Departamento de Geología y Minas e Ingeniería Civil, Universidad Técnica Particular de Loja, San Cayetano Alto s/n, 110107 Loja, Ecuador
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Remote Sens. 2017, 9(10), 1058; https://doi.org/10.3390/rs9101058
Received: 2 September 2017 / Revised: 3 October 2017 / Accepted: 12 October 2017 / Published: 17 October 2017
(This article belongs to the Section Remote Sensing Image Processing)
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, but do so when terrain features were taken into account. On the other hand, sequential maximum a posteriori increased accuracy when textural features were used, but reduced accuracy substantially when terrain features were included. Random forest using the three feature subsets and sequential maximum a posteriori with spectral and textural features had the largest kappa values, around 0.9. View Full-Text
Keywords: land use classification; machine learning; textural information; contextual information land use classification; machine learning; textural information; contextual information
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MDPI and ACS Style

Gomariz-Castillo, F.; Alonso-Sarría, F.; Cánovas-García, F. 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. Remote Sens. 2017, 9, 1058.

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