Paper Info
Title
Evaluation of different machine learning methods for land cover mapping of a Mediterranean area using multi-seasonal Landsat images and Digital Terrain Models.
Abstract
Land cover monitoring using digital Earth data requires robust classification methods that allow the accurate mapping of complex land cover categories. This paper discusses the crucial issues related to the application of different up-to-date machine learning classifiers: classification trees (CT), artificial neural networks (ANN), support vector machines (SVM) and random forest (RF). The analysis of the statistical significance of the differences between the performance of these algorithms, as well as sensitivity to data set size reduction and noise were also analysed. Landsat-5 Thematic Mapper data captured in European spring and summer were used with auxiliary variables derived from a digital terrain model to classify 14 different land cover categories in south Spain. Overall, statistically similar accuracies of over 91% were obtained for ANN, SVM and RF. However, the findings of this study show differences in the accuracy of the classifiers, being RF the most accurate classifier with a very simple parameterization. SVM, followed by RF, was the most robust classifier to noise and data reduction. Significant differences in their performances were only reached for thresholds of noise and data reduction greater than 20% (noise, SVM) and 25% (noise, RF), and 80% (reduction, SVM) and 50% (reduction, RF), respectively.
Year
DOI
Venue
2014
10.1080/17538947.2012.748848
INTERNATIONAL JOURNAL OF DIGITAL EARTH
Keywords
Field
DocType
random forest,classification trees,support vector machines,remote sensing,land cover,neural networks
Data mining,Thematic Mapper,Terrain,Remote sensing,Digital elevation model,Artificial intelligence,Artificial neural network,Random forest,Classifier (linguistics),Land cover,Geography,Support vector machine,Machine learning
Journal
Volume
Issue
ISSN
7.0
6.0
1753-8947
Citations 
PageRank 
References 
5
0.46
9
Authors
2
Name
Order
Citations
PageRank
V. F. Rodriguez-Galiano1394.09
Mario Chica-Rivas2292.22