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Hyperspectral Image Classification Using Isomap with SMACOF
dc.contributor.author | Orts Gómez, Francisco José | |
dc.contributor.author | Ortega López, Gloria | |
dc.contributor.author | Filatovas, Ernestas | |
dc.contributor.author | Kurasova, Olga | |
dc.contributor.author | Martín Garzón, Gracia Ester | |
dc.date.accessioned | 2024-01-30T12:08:39Z | |
dc.date.available | 2024-01-30T12:08:39Z | |
dc.date.issued | 2019-01-01 | |
dc.identifier.citation | Orts Gómez, F. J., Ortega López, G., Filatovas, E., Kurasova, O., & Garzón, G. E. M. (2019). Hyperspectral image classification using Isomap with SMACOF. Informatica, 30(2), 349-365. | es_ES |
dc.identifier.uri | http://hdl.handle.net/10835/15558 | |
dc.description.abstract | The isometric mapping (Isomap) algorithm is often used for analysing hyperspectral images. Isomap allows to reduce such hyperspectral images from a high-dimensional space into a lower-dimensional space, keeping the critical original information. To achieve such objective, Isomap uses the state-of-the-art MultiDimensional Scaling method (MDS) for dimensionality reduction. In this work, we propose to use Isomap with SMACOF, since SMACOF is the most accurate MDS method. A deep comparison, in terms of accuracy, between Isomap based on an eigen-decomposition process and Isomap based on SMACOF has been carried out using three benchmark hyperspectral images. Moreover, for the hyperspectral image classification, three classifiers (support vector machine, k-nearest neighbour, and Random Forest) have been used to compare both Isomap approaches. The experimental investigation has shown that better classification accuracy is obtained by Isomap with SMACOF. | es_ES |
dc.language.iso | en | es_ES |
dc.publisher | Informatica | es_ES |
dc.title | Hyperspectral Image Classification Using Isomap with SMACOF | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.relation.publisherversion | https://informatica.vu.lt/journal/INFORMATICA/article/1119/info | es_ES |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es_ES |
dc.identifier.doi | 10.15388/Informatica.2019.209 |