Novel Image Classification technique using Particle Filter Framework optimised by Multikernel Sparse Representation
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Brazilian Archives of Biology and Technology |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132016000300606 |
Resumo: | ABSTRACT The robustness and speed of image classification is still a challenging task in satellite image processing. This paper introduces a novel image classification technique that uses the particle filter framework (PFF)-based optimisation technique for satellite image classification. The framework uses a template-matching algorithm, comprising fast marching algorithm (FMA) and level set method (LSM)-based segmentation which assists in creating the initial templates for comparison with other test images. The created templates are trained and used as inputs for the optimisation. The optimisation technique used in this proposed work is multikernel sparse representation (MKSR). The combined execution of FMA, LSM, PFF and MKSR approaches has resulted in a substantial reduction in processing time for various classes in a satellite image which is small when compared with Support Vector Machine (SVM) and Independent Component Discrimination Analysis (ICDA)based image classifications obtained for comparison purposes. This study aims to improve the robustness of image classification based on overall accuracy (OA) and kappa coefficient. The variation of OA with this technique, between different classes of a satellite image, is only10%, whereas that with the SVM and ICDA techniques is more than 50%. |
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Brazilian Archives of Biology and Technology |
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Novel Image Classification technique using Particle Filter Framework optimised by Multikernel Sparse RepresentationMultikernel Sparse RepresentationImage ClassificationSparse LearningLevel Set MethodParticle Filter FrameworkRemote SensingABSTRACT The robustness and speed of image classification is still a challenging task in satellite image processing. This paper introduces a novel image classification technique that uses the particle filter framework (PFF)-based optimisation technique for satellite image classification. The framework uses a template-matching algorithm, comprising fast marching algorithm (FMA) and level set method (LSM)-based segmentation which assists in creating the initial templates for comparison with other test images. The created templates are trained and used as inputs for the optimisation. The optimisation technique used in this proposed work is multikernel sparse representation (MKSR). The combined execution of FMA, LSM, PFF and MKSR approaches has resulted in a substantial reduction in processing time for various classes in a satellite image which is small when compared with Support Vector Machine (SVM) and Independent Component Discrimination Analysis (ICDA)based image classifications obtained for comparison purposes. This study aims to improve the robustness of image classification based on overall accuracy (OA) and kappa coefficient. The variation of OA with this technique, between different classes of a satellite image, is only10%, whereas that with the SVM and ICDA techniques is more than 50%.Instituto de Tecnologia do Paraná - Tecpar2016-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132016000300606Brazilian Archives of Biology and Technology v.59 n.spe2 2016reponame:Brazilian Archives of Biology and Technologyinstname:Instituto de Tecnologia do Paraná (Tecpar)instacron:TECPAR10.1590/1678-4324-2016161052info:eu-repo/semantics/openAccessN. R,BhuvaneswariV.G,Sivakumareng2017-01-19T00:00:00Zoai:scielo:S1516-89132016000300606Revistahttps://www.scielo.br/j/babt/https://old.scielo.br/oai/scielo-oai.phpbabt@tecpar.br||babt@tecpar.br1678-43241516-8913opendoar:2017-01-19T00:00Brazilian Archives of Biology and Technology - Instituto de Tecnologia do Paraná (Tecpar)false |
dc.title.none.fl_str_mv |
Novel Image Classification technique using Particle Filter Framework optimised by Multikernel Sparse Representation |
title |
Novel Image Classification technique using Particle Filter Framework optimised by Multikernel Sparse Representation |
spellingShingle |
Novel Image Classification technique using Particle Filter Framework optimised by Multikernel Sparse Representation N. R,Bhuvaneswari Multikernel Sparse Representation Image Classification Sparse Learning Level Set Method Particle Filter Framework Remote Sensing |
title_short |
Novel Image Classification technique using Particle Filter Framework optimised by Multikernel Sparse Representation |
title_full |
Novel Image Classification technique using Particle Filter Framework optimised by Multikernel Sparse Representation |
title_fullStr |
Novel Image Classification technique using Particle Filter Framework optimised by Multikernel Sparse Representation |
title_full_unstemmed |
Novel Image Classification technique using Particle Filter Framework optimised by Multikernel Sparse Representation |
title_sort |
Novel Image Classification technique using Particle Filter Framework optimised by Multikernel Sparse Representation |
author |
N. R,Bhuvaneswari |
author_facet |
N. R,Bhuvaneswari V.G,Sivakumar |
author_role |
author |
author2 |
V.G,Sivakumar |
author2_role |
author |
dc.contributor.author.fl_str_mv |
N. R,Bhuvaneswari V.G,Sivakumar |
dc.subject.por.fl_str_mv |
Multikernel Sparse Representation Image Classification Sparse Learning Level Set Method Particle Filter Framework Remote Sensing |
topic |
Multikernel Sparse Representation Image Classification Sparse Learning Level Set Method Particle Filter Framework Remote Sensing |
description |
ABSTRACT The robustness and speed of image classification is still a challenging task in satellite image processing. This paper introduces a novel image classification technique that uses the particle filter framework (PFF)-based optimisation technique for satellite image classification. The framework uses a template-matching algorithm, comprising fast marching algorithm (FMA) and level set method (LSM)-based segmentation which assists in creating the initial templates for comparison with other test images. The created templates are trained and used as inputs for the optimisation. The optimisation technique used in this proposed work is multikernel sparse representation (MKSR). The combined execution of FMA, LSM, PFF and MKSR approaches has resulted in a substantial reduction in processing time for various classes in a satellite image which is small when compared with Support Vector Machine (SVM) and Independent Component Discrimination Analysis (ICDA)based image classifications obtained for comparison purposes. This study aims to improve the robustness of image classification based on overall accuracy (OA) and kappa coefficient. The variation of OA with this technique, between different classes of a satellite image, is only10%, whereas that with the SVM and ICDA techniques is more than 50%. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-01-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132016000300606 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132016000300606 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/1678-4324-2016161052 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Instituto de Tecnologia do Paraná - Tecpar |
publisher.none.fl_str_mv |
Instituto de Tecnologia do Paraná - Tecpar |
dc.source.none.fl_str_mv |
Brazilian Archives of Biology and Technology v.59 n.spe2 2016 reponame:Brazilian Archives of Biology and Technology instname:Instituto de Tecnologia do Paraná (Tecpar) instacron:TECPAR |
instname_str |
Instituto de Tecnologia do Paraná (Tecpar) |
instacron_str |
TECPAR |
institution |
TECPAR |
reponame_str |
Brazilian Archives of Biology and Technology |
collection |
Brazilian Archives of Biology and Technology |
repository.name.fl_str_mv |
Brazilian Archives of Biology and Technology - Instituto de Tecnologia do Paraná (Tecpar) |
repository.mail.fl_str_mv |
babt@tecpar.br||babt@tecpar.br |
_version_ |
1750318277797609472 |