dc.creator | Carranza García, Manuel | es |
dc.creator | García Gutiérrez, Jorge | es |
dc.creator | Riquelme Santos, José Cristóbal | es |
dc.date.accessioned | 2019-08-27T08:48:54Z | |
dc.date.available | 2019-08-27T08:48:54Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | Carranza García, M., García Gutiérrez, J. y Riquelme Santos, J.C. (2019). A Framework for Evaluating Land Use and Land Cover Classification Using Convolutional Neural Networks. Remote sensing, 11 (3-274) | |
dc.identifier.issn | 2072-4292 | es |
dc.identifier.uri | https://hdl.handle.net/11441/88714 | |
dc.description.abstract | Analyzing land use and land cover (LULC) using remote sensing (RS) imagery is essential
for many environmental and social applications. The increase in availability of RS data has led to the
development of new techniques for digital pattern classification. Very recently, deep learning (DL)
models have emerged as a powerful solution to approach many machine learning (ML) problems.
In particular, convolutional neural networks (CNNs) are currently the state of the art for many image
classification tasks. While there exist several promising proposals on the application of CNNs to
LULC classification, the validation framework proposed for the comparison of different methods
could be improved with the use of a standard validation procedure for ML based on cross-validation
and its subsequent statistical analysis. In this paper, we propose a general CNN, with a fixed
architecture and parametrization, to achieve high accuracy on LULC classification over RS data
from different sources such as radar and hyperspectral. We also present a methodology to perform
a rigorous experimental comparison between our proposed DL method and other ML algorithms
such as support vector machines, random forests, and k-nearest-neighbors. The analysis carried out
demonstrates that the CNN outperforms the rest of techniques, achieving a high level of performance
for all the datasets studied, regardless of their different characteristics. | es |
dc.description.sponsorship | Ministerio de Economía y Competitividad TIN2014-55894-C2-1-R | es |
dc.description.sponsorship | Ministerio de Economía y Competitividad TIN2017-88209-C2-2-R | es |
dc.format | application/pdf | es |
dc.language.iso | eng | es |
dc.publisher | MDPI | es |
dc.relation.ispartof | Remote sensing, 11 (3-274) | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Convolutional neural network | es |
dc.subject | Cross-validation | es |
dc.subject | Deep learning | es |
dc.subject | Land use classification | es |
dc.subject | Land cover classification | es |
dc.subject | Remote sensing | es |
dc.subject | Statistical analysis | es |
dc.title | A Framework for Evaluating Land Use and Land Cover Classification Using Convolutional Neural Networks | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos | es |
dc.relation.projectID | TIN2014-55894-C2-1-R | es |
dc.relation.projectID | TIN2017-88209-C2-2-R | es |
dc.relation.publisherversion | https://www.mdpi.com/2072-4292/11/3/274 | es |
dc.identifier.doi | 10.3390/rs11030274 | es |
idus.format.extent | 23 | es |
dc.journaltitle | Remote sensing | es |
dc.publication.volumen | 11 | es |
dc.publication.issue | 3-274 | es |