A machine learning methodology for land use/land cover classification in tropical areas using medium resolution satellite imagery, case: Colombia
ABSTRACT : In this work, we first present a methodology for preparing 10 m to 60 m spatial resolution Sentinel-1, Sentinel-2, and ALOS DSM imagery of forest/grassland areas in Colombia to train a DeepLabV3+ convolutional neural network model. Our preprocessing pipeline for the Sentinel-2 imagery com...
- Autores:
-
Ceballos Arroyo, Alberto Mario
- Tipo de recurso:
- Tesis
- Fecha de publicación:
- 2021
- Institución:
- Universidad de Antioquia
- Repositorio:
- Repositorio UdeA
- Idioma:
- eng
- OAI Identifier:
- oai:bibliotecadigital.udea.edu.co:10495/20659
- Acceso en línea:
- http://hdl.handle.net/10495/20659
- Palabra clave:
- Remote sensing
Teledetección
Machine learning
Aprendizaje electrónico
Imágenes por satélites
Satellite imagery
Redes de neuronas
Neural networks
Tratamiento de imágenes
Image processing
Deep Learning
Sentinel-2
Convolutional Neural Network
Satellite Imagery
http://aims.fao.org/aos/agrovoc/c_49834
http://aims.fao.org/aos/agrovoc/c_37359
http://aims.fao.org/aos/agrovoc/c_36761
http://aims.fao.org/aos/agrovoc/c_37467
http://vocabularies.unesco.org/thesaurus/concept1557
- Rights
- openAccess
- License
- http://creativecommons.org/publicdomain/zero/1.0/
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|
dc.title.spa.fl_str_mv |
A machine learning methodology for land use/land cover classification in tropical areas using medium resolution satellite imagery, case: Colombia |
title |
A machine learning methodology for land use/land cover classification in tropical areas using medium resolution satellite imagery, case: Colombia |
spellingShingle |
A machine learning methodology for land use/land cover classification in tropical areas using medium resolution satellite imagery, case: Colombia Remote sensing Teledetección Machine learning Aprendizaje electrónico Imágenes por satélites Satellite imagery Redes de neuronas Neural networks Tratamiento de imágenes Image processing Deep Learning Sentinel-2 Convolutional Neural Network Satellite Imagery http://aims.fao.org/aos/agrovoc/c_49834 http://aims.fao.org/aos/agrovoc/c_37359 http://aims.fao.org/aos/agrovoc/c_36761 http://aims.fao.org/aos/agrovoc/c_37467 http://vocabularies.unesco.org/thesaurus/concept1557 |
title_short |
A machine learning methodology for land use/land cover classification in tropical areas using medium resolution satellite imagery, case: Colombia |
title_full |
A machine learning methodology for land use/land cover classification in tropical areas using medium resolution satellite imagery, case: Colombia |
title_fullStr |
A machine learning methodology for land use/land cover classification in tropical areas using medium resolution satellite imagery, case: Colombia |
title_full_unstemmed |
A machine learning methodology for land use/land cover classification in tropical areas using medium resolution satellite imagery, case: Colombia |
title_sort |
A machine learning methodology for land use/land cover classification in tropical areas using medium resolution satellite imagery, case: Colombia |
dc.creator.fl_str_mv |
Ceballos Arroyo, Alberto Mario |
dc.contributor.advisor.none.fl_str_mv |
Ramos Pollán, Raul |
dc.contributor.author.none.fl_str_mv |
Ceballos Arroyo, Alberto Mario |
dc.subject.unesco.none.fl_str_mv |
Remote sensing Teledetección |
topic |
Remote sensing Teledetección Machine learning Aprendizaje electrónico Imágenes por satélites Satellite imagery Redes de neuronas Neural networks Tratamiento de imágenes Image processing Deep Learning Sentinel-2 Convolutional Neural Network Satellite Imagery http://aims.fao.org/aos/agrovoc/c_49834 http://aims.fao.org/aos/agrovoc/c_37359 http://aims.fao.org/aos/agrovoc/c_36761 http://aims.fao.org/aos/agrovoc/c_37467 http://vocabularies.unesco.org/thesaurus/concept1557 |
dc.subject.agrovoc.none.fl_str_mv |
Machine learning Aprendizaje electrónico Imágenes por satélites Satellite imagery Redes de neuronas Neural networks Tratamiento de imágenes Image processing |
dc.subject.proposal.spa.fl_str_mv |
Deep Learning Sentinel-2 Convolutional Neural Network Satellite Imagery |
dc.subject.agrovocuri.none.fl_str_mv |
http://aims.fao.org/aos/agrovoc/c_49834 http://aims.fao.org/aos/agrovoc/c_37359 http://aims.fao.org/aos/agrovoc/c_36761 http://aims.fao.org/aos/agrovoc/c_37467 |
dc.subject.unescouri.none.fl_str_mv |
http://vocabularies.unesco.org/thesaurus/concept1557 |
description |
ABSTRACT : In this work, we first present a methodology for preparing 10 m to 60 m spatial resolution Sentinel-1, Sentinel-2, and ALOS DSM imagery of forest/grassland areas in Colombia to train a DeepLabV3+ convolutional neural network model. Our preprocessing pipeline for the Sentinel-2 imagery comprises cloud and shadow removal, atmospheric correction, and topographical correction, resulting in mostly cloud-free mosaics of tropical areas. At first, we train the network on very low spatial resolution (500 m) labels of the Colombian Amazonas region resampled to 10 m (+100000 samples after augmentation). Then, we fine-tune the network on medium spatial resolution data (30 m) of northern Antioquia, also resampled to 10 m, resulting in faster convergence and higher accuracy despite the limited number of labelled samples (~5000 samples after augmentation). Our results validate recent proposals where low spatial resolution data is used for training neural networks, and motivate us to keep exploring this line of research. |
publishDate |
2021 |
dc.date.accessioned.none.fl_str_mv |
2021-07-06T21:38:06Z |
dc.date.available.none.fl_str_mv |
2021-07-06T21:38:06Z |
dc.date.issued.none.fl_str_mv |
2021 |
dc.type.spa.fl_str_mv |
info:eu-repo/semantics/other |
dc.type.coarversion.fl_str_mv |
http://purl.org/coar/version/c_b1a7d7d4d402bcce |
dc.type.hasversion.spa.fl_str_mv |
info:eu-repo/semantics/draft |
dc.type.coar.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_46ec |
dc.type.redcol.spa.fl_str_mv |
http://purl.org/redcol/resource_type/COther |
dc.type.local.spa.fl_str_mv |
Tesis/Trabajo de grado - Monografía - Especialización |
format |
http://purl.org/coar/resource_type/c_46ec |
status_str |
draft |
dc.identifier.uri.none.fl_str_mv |
http://hdl.handle.net/10495/20659 |
url |
http://hdl.handle.net/10495/20659 |
dc.language.iso.spa.fl_str_mv |
eng |
language |
eng |
dc.rights.spa.fl_str_mv |
info:eu-repo/semantics/openAccess |
dc.rights.uri.*.fl_str_mv |
http://creativecommons.org/publicdomain/zero/1.0/ |
dc.rights.accessrights.spa.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
dc.rights.creativecommons.spa.fl_str_mv |
https://creativecommons.org/licenses/by-nc-sa/4.0/ |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
http://creativecommons.org/publicdomain/zero/1.0/ http://purl.org/coar/access_right/c_abf2 https://creativecommons.org/licenses/by-nc-sa/4.0/ |
dc.format.extent.spa.fl_str_mv |
22 |
dc.format.mimetype.spa.fl_str_mv |
pdf |
dc.publisher.place.spa.fl_str_mv |
Medellín, Colombia |
institution |
Universidad de Antioquia |
bitstream.url.fl_str_mv |
http://bibliotecadigital.udea.edu.co/bitstream/10495/20659/2/Ceballos_Alberto_2021_ML_LULC_Colombia.pdf http://bibliotecadigital.udea.edu.co/bitstream/10495/20659/3/license_rdf http://bibliotecadigital.udea.edu.co/bitstream/10495/20659/6/license.txt |
bitstream.checksum.fl_str_mv |
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bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 |
repository.name.fl_str_mv |
Repositorio Institucional Universidad de Antioquia |
repository.mail.fl_str_mv |
andres.perez@udea.edu.co |
_version_ |
1812173227944837120 |
spelling |
Ramos Pollán, RaulCeballos Arroyo, Alberto Mario2021-07-06T21:38:06Z2021-07-06T21:38:06Z2021http://hdl.handle.net/10495/20659ABSTRACT : In this work, we first present a methodology for preparing 10 m to 60 m spatial resolution Sentinel-1, Sentinel-2, and ALOS DSM imagery of forest/grassland areas in Colombia to train a DeepLabV3+ convolutional neural network model. Our preprocessing pipeline for the Sentinel-2 imagery comprises cloud and shadow removal, atmospheric correction, and topographical correction, resulting in mostly cloud-free mosaics of tropical areas. At first, we train the network on very low spatial resolution (500 m) labels of the Colombian Amazonas region resampled to 10 m (+100000 samples after augmentation). Then, we fine-tune the network on medium spatial resolution data (30 m) of northern Antioquia, also resampled to 10 m, resulting in faster convergence and higher accuracy despite the limited number of labelled samples (~5000 samples after augmentation). Our results validate recent proposals where low spatial resolution data is used for training neural networks, and motivate us to keep exploring this line of research.22pdfenginfo:eu-repo/semantics/draftinfo:eu-repo/semantics/otherhttp://purl.org/coar/resource_type/c_46echttp://purl.org/redcol/resource_type/COtherTesis/Trabajo de grado - Monografía - Especializaciónhttp://purl.org/coar/version/c_b1a7d7d4d402bcceinfo:eu-repo/semantics/openAccesshttp://creativecommons.org/publicdomain/zero/1.0/http://purl.org/coar/access_right/c_abf2https://creativecommons.org/licenses/by-nc-sa/4.0/A machine learning methodology for land use/land cover classification in tropical areas using medium resolution satellite imagery, case: ColombiaMedellín, ColombiaRemote sensingTeledetecciónMachine learningAprendizaje electrónicoImágenes por satélitesSatellite imageryRedes de neuronasNeural networksTratamiento de imágenesImage processingDeep LearningSentinel-2Convolutional Neural NetworkSatellite Imageryhttp://aims.fao.org/aos/agrovoc/c_49834http://aims.fao.org/aos/agrovoc/c_37359http://aims.fao.org/aos/agrovoc/c_36761http://aims.fao.org/aos/agrovoc/c_37467http://vocabularies.unesco.org/thesaurus/concept1557https://drive.google.com/file/d/1uYiQiuiUTjwVbnYwZTNRQJpLWR-XFYtc/view?usp=sharingEspecialista en Analítica y Ciencia de DatosEspecializaciónFacultad de Ingeniería. Especialización en Analítica y Ciencia de DatosUniversidad de AntioquiaORIGINALCeballos_Alberto_2021_ML_LULC_Colombia.pdfCeballos_Alberto_2021_ML_LULC_Colombia.pdfTrabajo de grado de especializaciónapplication/pdf2065897http://bibliotecadigital.udea.edu.co/bitstream/10495/20659/2/Ceballos_Alberto_2021_ML_LULC_Colombia.pdf79b6cabd5ad872427257694e511d20f4MD52CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8712http://bibliotecadigital.udea.edu.co/bitstream/10495/20659/3/license_rdffd0548b8694973befb689f3e7a707f1dMD53LICENSElicense.txtlicense.txttext/plain; charset=utf-81748http://bibliotecadigital.udea.edu.co/bitstream/10495/20659/6/license.txt8a4605be74aa9ea9d79846c1fba20a33MD5610495/20659oai:bibliotecadigital.udea.edu.co:10495/206592021-07-06 16:41:24.937Repositorio Institucional Universidad de Antioquiaandres.perez@udea.edu.coTk9URTogUExBQ0UgWU9VUiBPV04gTElDRU5TRSBIRVJFClRoaXMgc2FtcGxlIGxpY2Vuc2UgaXMgcHJvdmlkZWQgZm9yIGluZm9ybWF0aW9uYWwgcHVycG9zZXMgb25seS4KCk5PTi1FWENMVVNJVkUgRElTVFJJQlVUSU9OIExJQ0VOU0UKCkJ5IHNpZ25pbmcgYW5kIHN1Ym1pdHRpbmcgdGhpcyBsaWNlbnNlLCB5b3UgKHRoZSBhdXRob3Iocykgb3IgY29weXJpZ2h0Cm93bmVyKSBncmFudHMgdG8gRFNwYWNlIFVuaXZlcnNpdHkgKERTVSkgdGhlIG5vbi1leGNsdXNpdmUgcmlnaHQgdG8gcmVwcm9kdWNlLAp0cmFuc2xhdGUgKGFzIGRlZmluZWQgYmVsb3cpLCBhbmQvb3IgZGlzdHJpYnV0ZSB5b3VyIHN1Ym1pc3Npb24gKGluY2x1ZGluZwp0aGUgYWJzdHJhY3QpIHdvcmxkd2lkZSBpbiBwcmludCBhbmQgZWxlY3Ryb25pYyBmb3JtYXQgYW5kIGluIGFueSBtZWRpdW0sCmluY2x1ZGluZyBidXQgbm90IGxpbWl0ZWQgdG8gYXVkaW8gb3IgdmlkZW8uCgpZb3UgYWdyZWUgdGhhdCBEU1UgbWF5LCB3aXRob3V0IGNoYW5naW5nIHRoZSBjb250ZW50LCB0cmFuc2xhdGUgdGhlCnN1Ym1pc3Npb24gdG8gYW55IG1lZGl1bSBvciBmb3JtYXQgZm9yIHRoZSBwdXJwb3NlIG9mIHByZXNlcnZhdGlvbi4KCllvdSBhbHNvIGFncmVlIHRoYXQgRFNVIG1heSBrZWVwIG1vcmUgdGhhbiBvbmUgY29weSBvZiB0aGlzIHN1Ym1pc3Npb24gZm9yCnB1cnBvc2VzIG9mIHNlY3VyaXR5LCBiYWNrLXVwIGFuZCBwcmVzZXJ2YXRpb24uCgpZb3UgcmVwcmVzZW50IHRoYXQgdGhlIHN1Ym1pc3Npb24gaXMgeW91ciBvcmlnaW5hbCB3b3JrLCBhbmQgdGhhdCB5b3UgaGF2ZQp0aGUgcmlnaHQgdG8gZ3JhbnQgdGhlIHJpZ2h0cyBjb250YWluZWQgaW4gdGhpcyBsaWNlbnNlLiBZb3UgYWxzbyByZXByZXNlbnQKdGhhdCB5b3VyIHN1Ym1pc3Npb24gZG9lcyBub3QsIHRvIHRoZSBiZXN0IG9mIHlvdXIga25vd2xlZGdlLCBpbmZyaW5nZSB1cG9uCmFueW9uZSdzIGNvcHlyaWdodC4KCklmIHRoZSBzdWJtaXNzaW9uIGNvbnRhaW5zIG1hdGVyaWFsIGZvciB3aGljaCB5b3UgZG8gbm90IGhvbGQgY29weXJpZ2h0LAp5b3UgcmVwcmVzZW50IHRoYXQgeW91IGhhdmUgb2J0YWluZWQgdGhlIHVucmVzdHJpY3RlZCBwZXJtaXNzaW9uIG9mIHRoZQpjb3B5cmlnaHQgb3duZXIgdG8gZ3JhbnQgRFNVIHRoZSByaWdodHMgcmVxdWlyZWQgYnkgdGhpcyBsaWNlbnNlLCBhbmQgdGhhdApzdWNoIHRoaXJkLXBhcnR5IG93bmVkIG1hdGVyaWFsIGlzIGNsZWFybHkgaWRlbnRpZmllZCBhbmQgYWNrbm93bGVkZ2VkCndpdGhpbiB0aGUgdGV4dCBvciBjb250ZW50IG9mIHRoZSBzdWJtaXNzaW9uLgoKSUYgVEhFIFNVQk1JU1NJT04gSVMgQkFTRUQgVVBPTiBXT1JLIFRIQVQgSEFTIEJFRU4gU1BPTlNPUkVEIE9SIFNVUFBPUlRFRApCWSBBTiBBR0VOQ1kgT1IgT1JHQU5JWkFUSU9OIE9USEVSIFRIQU4gRFNVLCBZT1UgUkVQUkVTRU5UIFRIQVQgWU9VIEhBVkUKRlVMRklMTEVEIEFOWSBSSUdIVCBPRiBSRVZJRVcgT1IgT1RIRVIgT0JMSUdBVElPTlMgUkVRVUlSRUQgQlkgU1VDSApDT05UUkFDVCBPUiBBR1JFRU1FTlQuCgpEU1Ugd2lsbCBjbGVhcmx5IGlkZW50aWZ5IHlvdXIgbmFtZShzKSBhcyB0aGUgYXV0aG9yKHMpIG9yIG93bmVyKHMpIG9mIHRoZQpzdWJtaXNzaW9uLCBhbmQgd2lsbCBub3QgbWFrZSBhbnkgYWx0ZXJhdGlvbiwgb3RoZXIgdGhhbiBhcyBhbGxvd2VkIGJ5IHRoaXMKbGljZW5zZSwgdG8geW91ciBzdWJtaXNzaW9uLgo= |