Publication
Using LUCAS survey and Recurrent Neural Networks to produce LCLU classification based on a Satellite Image time series of Sentinel-2
| Summary: | The need of timely and accurate information for the territory has increased over the years, making Land Cover Land Use (LCLU) mapping one of the most common application of remote sensing. Recently, the advances in satellite technology and the open access policies for remote sensing data increased the interest in exploring satellite image time series. In addition, the attention of researchers has shifted from standard machine learning algorithms (e.g., Support Vector Machines and Random Forest) to Recurrent Neural Networks due to their ability of exploiting sequential information. However, acquiring reference data to train these algorithms is still a hurdle. This study aims to evaluate the capability of a Gated Recurrent Unit in performing pixel-level LCLU classification of a satellite image time series, using Sentinel-2 imagery and having the LUCAS survey as reference data. To assess the performance of our model we compared it to state-of-the-art classifiers (SVM and RF). Due to the unbalance nature of the LUCAS survey, we applied oversampling to this dataset to increase the performance of our models, testing three different oversampling techniques. The results attained showed that Recurrent Neural Networks did not outperform the other state-of-the-art algorithms, when trained with a limited number of sampling units, and that oversampling the LUCAS survey increased the performance of all the classifiers. Finally, we were able to demonstrate that it is possible to produce LCLU classification of satellite image time series using only open-source data by using Sentinel-2 imagery and the LUCAS survey as refence data. |
|---|---|
| Main Authors: | Silva, Nuno Alexandre Pereira da |
| Subject: | LCLU classification LUCAS survey Recurrent Neural Networks Oversampling Sentinel-2 |
| Year: | 2021 |
| Country: | Portugal |
| Document type: | master thesis |
| Access type: | open access |
| Associated institution: | Universidade Nova de Lisboa |
| Language: | English |
| Origin: | Repositório Institucional da UNL |
| _version_ | 1868983616808157184 |
|---|---|
| author | Silva, Nuno Alexandre Pereira da |
| author_facet | Silva, Nuno Alexandre Pereira da |
| author_role | author |
| contributor_name_str_mv | Caetano, Mário Sílvio Rochinha de Andrade Castelli, Mauro RUN |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Silva, Nuno Alexandre Pereira da\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | Caetano, Mário Sílvio Rochinha de Andrade Castelli, Mauro RUN |
| datacite.creators.creator.creatorName.fl_str_mv | Silva, Nuno Alexandre Pereira da |
| datacite.date.Accepted.fl_str_mv | 2021-06-22T00:00:00Z |
| datacite.date.available.fl_str_mv | 2021-08-23T16:14:30Z |
| datacite.date.embargoed.fl_str_mv | 2021-08-23T16:14:30Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | LCLU classification LUCAS survey Recurrent Neural Networks Oversampling Sentinel-2 |
| datacite.titles.title.fl_str_mv | Using LUCAS survey and Recurrent Neural Networks to produce LCLU classification based on a Satellite Image time series of Sentinel-2 |
| dc.contributor.none.fl_str_mv | Caetano, Mário Sílvio Rochinha de Andrade Castelli, Mauro RUN |
| dc.creator.none.fl_str_mv | Silva, Nuno Alexandre Pereira da |
| dc.date.Accepted.fl_str_mv | 2021-06-22T00:00:00Z |
| dc.date.available.fl_str_mv | 2021-08-23T16:14:30Z |
| dc.date.embargoed.fl_str_mv | 2021-08-23T16:14:30Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10362/122980 |
| dc.language.none.fl_str_mv | eng |
| dc.rights.cclincense.fl_str_mv | http://creativecommons.org/licenses/by/4.0/ |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.subject.none.fl_str_mv | LCLU classification LUCAS survey Recurrent Neural Networks Oversampling Sentinel-2 |
| dc.title.fl_str_mv | Using LUCAS survey and Recurrent Neural Networks to produce LCLU classification based on a Satellite Image time series of Sentinel-2 |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_bdcc |
| description | The need of timely and accurate information for the territory has increased over the years, making Land Cover Land Use (LCLU) mapping one of the most common application of remote sensing. Recently, the advances in satellite technology and the open access policies for remote sensing data increased the interest in exploring satellite image time series. In addition, the attention of researchers has shifted from standard machine learning algorithms (e.g., Support Vector Machines and Random Forest) to Recurrent Neural Networks due to their ability of exploiting sequential information. However, acquiring reference data to train these algorithms is still a hurdle. This study aims to evaluate the capability of a Gated Recurrent Unit in performing pixel-level LCLU classification of a satellite image time series, using Sentinel-2 imagery and having the LUCAS survey as reference data. To assess the performance of our model we compared it to state-of-the-art classifiers (SVM and RF). Due to the unbalance nature of the LUCAS survey, we applied oversampling to this dataset to increase the performance of our models, testing three different oversampling techniques. The results attained showed that Recurrent Neural Networks did not outperform the other state-of-the-art algorithms, when trained with a limited number of sampling units, and that oversampling the LUCAS survey increased the performance of all the classifiers. Finally, we were able to demonstrate that it is possible to produce LCLU classification of satellite image time series using only open-source data by using Sentinel-2 imagery and the LUCAS survey as refence data. |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | masterThesis |
| fulltext.url.fl_str_mv | https://run.unl.pt/bitstreams/c3791026-1d47-4f9a-92b4-0231d4bf9800/download |
| id | run_8aee2d0a8ce8e79137c83e47396ba2bc |
| identifier.url.fl_str_mv | http://hdl.handle.net/10362/122980 |
| inst_facet_str | urn:organizationAcronym:unl{{{_:::_}}}Universidade Nova de Lisboa |
| instacron_str | unl |
| institution | Universidade Nova de Lisboa |
| instname_str | Universidade Nova de Lisboa |
| language | eng |
| network_acronym_str | run |
| network_name_str | Repositório Institucional da UNL |
| oai_identifier_str | oai:run.unl.pt:10362/122980 |
| organization_str_mv | urn:organizationAcronym:unl |
| person_str_mv | Silva, Nuno Alexandre Pereira da |
| publishDate | 2021 |
| repo_facet_str | urn:repositoryAcronym:run{{{_:::_}}}Repositório Institucional da UNL |
| reponame_str | Repositório Institucional da UNL |
| repository_id_str | urn:repositoryAcronym:run |
| service_str_mv | urn:repositoryAcronym:run |
| spelling | engpt_PTThe need of timely and accurate information for the territory has increased over the years, making Land Cover Land Use (LCLU) mapping one of the most common application of remote sensing. Recently, the advances in satellite technology and the open access policies for remote sensing data increased the interest in exploring satellite image time series. In addition, the attention of researchers has shifted from standard machine learning algorithms (e.g., Support Vector Machines and Random Forest) to Recurrent Neural Networks due to their ability of exploiting sequential information. However, acquiring reference data to train these algorithms is still a hurdle. This study aims to evaluate the capability of a Gated Recurrent Unit in performing pixel-level LCLU classification of a satellite image time series, using Sentinel-2 imagery and having the LUCAS survey as reference data. To assess the performance of our model we compared it to state-of-the-art classifiers (SVM and RF). Due to the unbalance nature of the LUCAS survey, we applied oversampling to this dataset to increase the performance of our models, testing three different oversampling techniques. The results attained showed that Recurrent Neural Networks did not outperform the other state-of-the-art algorithms, when trained with a limited number of sampling units, and that oversampling the LUCAS survey increased the performance of all the classifiers. Finally, we were able to demonstrate that it is possible to produce LCLU classification of satellite image time series using only open-source data by using Sentinel-2 imagery and the LUCAS survey as refence data.application/pdfpt_PTUsing LUCAS survey and Recurrent Neural Networks to produce LCLU classification based on a Satellite Image time series of Sentinel-2Silva, Nuno Alexandre Pereira daCaetano, Mário Sílvio Rochinha de AndradeCastelli, MauroHostingInstitutionOrganizationalRUNe-mailmailto:run@unl.ptrun@unl.ptURNurn:tid:2027571962021-08-23T16:14:30Z2021-06-222021-06-22T00:00:00ZHandlehttp://hdl.handle.net/10362/122980http://purl.org/coar/access_right/c_abf2open accessLCLU classificationLUCAS surveyRecurrent Neural NetworksOversamplingSentinel-21134671 bytesliteraturehttp://purl.org/coar/resource_type/c_bdccmaster thesis2021-06-22http://creativecommons.org/licenses/by/4.0/http://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://run.unl.pt/bitstreams/c3791026-1d47-4f9a-92b4-0231d4bf9800/download |
| spellingShingle | Using LUCAS survey and Recurrent Neural Networks to produce LCLU classification based on a Satellite Image time series of Sentinel-2 Silva, Nuno Alexandre Pereira da LCLU classification LUCAS survey Recurrent Neural Networks Oversampling Sentinel-2 |
| status | SINGLETON |
| subject.fl_str_mv | LCLU classification LUCAS survey Recurrent Neural Networks Oversampling Sentinel-2 |
| title | Using LUCAS survey and Recurrent Neural Networks to produce LCLU classification based on a Satellite Image time series of Sentinel-2 |
| title_full | Using LUCAS survey and Recurrent Neural Networks to produce LCLU classification based on a Satellite Image time series of Sentinel-2 |
| title_fullStr | Using LUCAS survey and Recurrent Neural Networks to produce LCLU classification based on a Satellite Image time series of Sentinel-2 |
| title_full_unstemmed | Using LUCAS survey and Recurrent Neural Networks to produce LCLU classification based on a Satellite Image time series of Sentinel-2 |
| title_short | Using LUCAS survey and Recurrent Neural Networks to produce LCLU classification based on a Satellite Image time series of Sentinel-2 |
| title_sort | Using LUCAS survey and Recurrent Neural Networks to produce LCLU classification based on a Satellite Image time series of Sentinel-2 |
| topic | LCLU classification LUCAS survey Recurrent Neural Networks Oversampling Sentinel-2 |
| topic_facet | LCLU classification LUCAS survey Recurrent Neural Networks Oversampling Sentinel-2 |
| url | http://hdl.handle.net/10362/122980 |
| visible | 1 |