Publication:
Effect of the Synergetic Use of Sentinel-1, Sentinel-2, LiDAR and Derived Data in Land Cover Classification of a Semiarid Mediterranean Area Using Machine Learning Algorithms

Thumbnail Image
Files
2023_RemoteSensing.pdf(20.41 MB)
Date
2023-01-05
relationships.isAuthorOfPublication
relationships.isSecondaryAuthorOf
relationships.isDirectorOf
Authors
Valdivieso Ros, Carmen ; Alonso Sarria, Francisco ; Gomariz Castillo, Francisco
item.page.secondaryauthor
item.page.director
Publisher
Multidisciplinary Digital Publishing Institute
publication.page.editor
publication.page.department
Geografía
DOI
https://doi.org/ 10.3390/rs15020312
item.page.type
info:eu-repo/semantics/article
Description
© 2023 by the authors.. This document is made available under the CC-BY 4.0 license http://creativecommons.org/licenses/by /4.0/ This document is the published version of a published work that appeared in final form in Remote Sensing
Abstract
Land cover classification in semiarid areas is a difficult task that has been tackled using different strategies, such as the use of normalized indices, texture metrics, and the combination of images from different dates or different sensors. In this paper we present the results of an experiment using three sensors (Sentinel-1 SAR, Sentinel-2 MSI and LiDAR), four dates and different normalized indices and texture metrics to classify a semiarid area. Three machine learning algorithms were used: Random Forest, Support Vector Machines and Multilayer Perceptron; Maximum Likelihood was used as a baseline classifier. The synergetic use of all these sources resulted in a significant increase in accuracy, Random Forest being the model reaching the highest accuracy. However, the large amount of features (126) advises the use of feature selection to reduce this figure. After using Variance Inflation Factor and Random Forest feature importance, the amount of features was reduced to 62. The final overall accuracy obtained was 0.91 & PLUSMN; 0.005 (alpha = 0.05) and kappa index 0.898 & PLUSMN; 0.006 (alpha = 0.05). Most of the observed confusions are easily explicable and do not represent a significant difference in agronomic terms.
publication.page.subject
Random forest , Support vector machines , Multilayer perceptron , Feature selection , Sentinel missions , Multisensor
Citation
Remote Sensing, 15,312. 2023
URI
http://hdl.handle.net/10201/137317
item.page.embargo
Collections
Artículos
Ir a Estadísticas