Optimization based manifold embedding for hyperspectral image classification and visualization

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dc.authoridOzcan, Caner/0000-0002-2854-4005
dc.authoridYILDIRIM, Mehmet Zahid/0000-0003-2248-3683
dc.contributor.authorYildirim, Mehmet Zahid
dc.contributor.authorOzcan, Caner
dc.contributor.authorErsoy, Okan
dc.date.accessioned2024-09-29T16:03:01Z
dc.date.available2024-09-29T16:03:01Z
dc.date.issued2021
dc.departmentKarabük Üniversitesien_US
dc.description.abstractRemote sensing and interpretation of hyperspectral images are becoming an increasingly important field of research. High dimensional hyperspectral images consist of hundreds of bands and reflect the properties of different materials. The need for more detail about objects and the improvement of sensor resolutions have resulted in the generation of higher size hyperspectral data. Many years of research have shown that there are many difficulties in the pre-processing of these data due to their high dimensionality. Recent studies have revealed that manifold learning techniques are a very important solution to this problem. However, as the complexity of the data increases, the performance of these methods cannot reach a sufficient level. This letter proposes a particle swarm-based multidimensional field embedding method inspired by the force field formulation to increase the performance. Detailed comparative analyses of the proposed method were made for Botswana and Kennedy Space Center (KSC) data. It is also shared in the results of other popular datasets. Experimental results show that the proposed method is superior to existing manifold embedding methods in classification accuracy and visualization of hyperspectral data. In addition, an optimization-based solution is presented to the problem of parameter determination of existing embedding methods.en_US
dc.description.sponsorshipScientific Research Projects Unit of Karabuk University [FDT-2020-2315]; Scientific Technological Research Council of Turkey [120E404]en_US
dc.description.sponsorshipThis work was supported by the Scientific Research Projects Unit of Karabuk University [FDT-2020-2315]; Scientific Technological Research Council of Turkey [120E404].en_US
dc.identifier.doi10.1080/2150704X.2021.1974118
dc.identifier.endpage1166en_US
dc.identifier.issn2150-704X
dc.identifier.issn2150-7058
dc.identifier.issue11en_US
dc.identifier.scopus2-s2.0-85115201766en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage1158en_US
dc.identifier.urihttps://doi.org/10.1080/2150704X.2021.1974118
dc.identifier.urihttps://hdl.handle.net/20.500.14619/5839
dc.identifier.volume12en_US
dc.identifier.wosWOS:000696218300001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherTaylor & Francis Ltden_US
dc.relation.ispartofRemote Sensing Lettersen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectParticle Swarm Optimizationen_US
dc.subjectDimensionality Reductionen_US
dc.subjectNetworken_US
dc.titleOptimization based manifold embedding for hyperspectral image classification and visualizationen_US
dc.typeArticleen_US

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