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

Dataset

Description

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.

Earth sciences
Date made available24 Jun 2019
PublisherDANS easy
Date of data production8 Mar 2019

Cite this

Zafari, A. (Creator) (24 Jun 2019). Evaluating the Performance of a Random Forest Kernel for Land Cover Classification. DANS easy. 10.17026/dans-26r-wte6