Random Forest classification of Mediterranean land cover using multi-seasonal imagery and multi-seasonal texture

By Victor Francisco Rodriguez-Galiano, Mario Chica-Olmo, F. Abarca-hernandez, Peter M. Atkinson, Cheryl Stephen Jeganathan. 2012

565 citations recorded by the source corpus.

Subfields: Ecology, Environmental Engineering, Media Technology

View Paper: https://www.semanticscholar.org/paper/9fc8a16a777f6bf9b7b06ef9fff8408f980f512d

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In Ecology

In Environmental Engineering

In Media Technology

Citations in the corpus

Discrimination of crop types with TerraSAR-X-derived information (2015, unknown): “134 The ratio between the misclassifications and the total number of OOB elements contributes an 135 unbiased estimation of the generalization error (Rodriguez-Galiano et al. 2012).”
Random Forest Classification for Mangrove Land Cover Mapping Using Landsat 5 TM and Alos Palsar Imageries (2015, background): “The use of RF has become popular in the field of remote sensing for land cover mapping [4, 5].”
GLC_FCS30: global land-cover product with fine classification system at 30 m using time-series Landsat imagery (2020, background): “…researchers have found that the texture variables can significantly improve the classification accuracy for land-cover mapping (M. Li et al., 2017; Rodriguez-Galiano et al., 2012; Wang et al., 2015; Zhu et al., 2012), for example, Zhu et al. (2012) found that the import of Landsat-derived texture…”
Efficient corn and soybean mapping with temporal extendability: A multi-year experiment using Landsat imagery (2014, methodology): “…spectral features directly acquired from remote sensed images are capable of distinguishing corn and soybean fields with a SY training set, as indicated by traditional mapping efforts using tree classifiers (Rodriguez-Galiano et al., 2012; Stehman&Milliken, 2007;Wardlow &Egbert, 2008).”
Exploring pathways linking greenspace to health: Theoretical and methodological guidance (2017, background): “Vegetation indices on their own are also poor at identifying vegetation types (trees, grass and shrubs), but various machine learning algorithms and spectral and object classification approaches are emerging to solve this problem (Fan, 2013; Rodriguez-Galiano et al., 2012).”
Impact of training and validation sample selection on classification accuracy and accuracy assessment when using reference polygons in object-based classification (2013, methodology): “RF applies bagging or bootstrap aggregation (Breiman 2001) and has several attractive advantages that made it appropriate for use in this study: first, it has a good chance of randomness in two dimensions (both training data and feature selection) and tends to yield high accuracy (Breiman 2001); sec”
Urban Flood Mapping Based on Unmanned Aerial Vehicle Remote Sensing and Random Forest Classifier—A Case of Yuyao, China (2015, methodology): “Compared with other machine learning methods, such as support vector machine and artificial neural network, Random Forest has a relatively lower computational burden, and it is insensitive to multivariate linear variables and outliers [19].”
Land-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: evaluating the performance of random forest and support vector machines classifiers (2014, background): “…(Cingolani et al. 2004; Erbek, Özkan, and Taberner 2004; Otukei and Blaschke 2010; Ustin et al. 1996; Gislason, Benediktsson, and Sveinsson 2006; Rodriguez-Galiano et al. 2012; Duro, Franklin, and Dubé 2012; Sanchez-Hernandez, Boyd, and Foody 2007; Kavzoglu and Colkesen 2009b), there is a…”