Evaluating the Performance of a Random Forest Kernel for Land Cover Classification

DOI

In this research, we evaluate the pros and cons of using an RF-based kernel (RFK) in an SVM compared to using the conventional Radial Basis Function (RBF) kernel and standard RF classifier. A time series of seven multispectralWorldView-2 images acquired over Sukumba (Mali) and a single hyperspectral AVIRIS image acquired over Salinas Valley (CA, USA) are used to illustrate the analyses

Identifier
DOI https://doi.org/10.17026/dans-26r-wte6
Metadata Access https://phys-techsciences.datastations.nl/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.17026/dans-26r-wte6
Provenance
Creator A Zafari
Publisher DANS Data Station Phys-Tech Sciences
Contributor M Th Koelen
Publication Year 2019
Rights CC0 1.0; info:eu-repo/semantics/openAccess; http://creativecommons.org/publicdomain/zero/1.0
OpenAccess true
Contact M Th Koelen (Faculty of Geo-Information Science and Earth Observation)
Representation
Resource Type Dataset
Format application/zip; text/plain; application/matlab-mat; text/csv
Size 69691; 2483; 26552770; 4277; 20240447; 382248; 32265139; 610890; 3428024; 5496584; 291384; 2636984; 4228184; 224184; 5384; 16824; 4184; 12984
Version 2.0
Discipline Earth and Environmental Science; Environmental Research; Geosciences; Natural Sciences