| 1. Identity statement | |
| Reference Type | Journal Article |
| Site | plutao.sid.inpe.br |
| Holder Code | isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S |
| Identifier | 8JMKD3MGP3W/3UGCSB8 |
| Repository | sid.inpe.br/plutao/2019/12.03.14.07 (restricted access) |
| Last Update | 2019:12.06.14.06.19 (UTC) simone |
| Metadata Repository | sid.inpe.br/plutao/2019/12.03.14.07.09 |
| Metadata Last Update | 2022:01.04.01.31.11 (UTC) administrator |
| DOI | 10.1080/01431161.2019.1681600 |
| ISSN | 0143-1161 |
| Label | lattes: 1861914973833506 2 SötheAlScLiRoBeFe:2019:CoMaDe |
| Citation Key | SotheAlScLiCuBeFe:2020:CoMaDe |
| Title | A comparison of machine and deep learning algorithms applied to multisource data for a subtropical forest area classification  |
| Year | 2020 |
| Access Date | 2024, May 18 |
| Secondary Type | PRE PI |
| Number of Files | 1 |
| Size | 3715 KiB |
| 2. Context | |
| Author | 1 Sothe, Camile 2 Almeida, Cláudia Maria de 3 Schimalski, Marcos Benedito 4 Liesenberg, Veraldo 5 Cue, Laura Elena 6 Bermudez, José David 7 Feitosa, Raul Queiroz |
| Resume Identifier | 1 2 8JMKD3MGP5W/3C9JGS3 |
| Group | 1 SER-SRE-SESPG-INPE-MCTIC-GOV-BR 2 DIDSR-CGOBT-INPE-MCTIC-GOV-BR |
| Affiliation | 1 Instituto Nacional de Pesquisas Espaciais (INPE) 2 Instituto Nacional de Pesquisas Espaciais (INPE) 3 Universidade do Estado de Santa Catarina (UDESC) 4 Universidade do Estado de Santa Catarina (UDESC) 5 Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio) 6 Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio) 7 Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio) |
| Author e-Mail Address | 1 camile.sothe@inpe.br 2 claudia.almeida@inpe.br |
| Journal | International Journal of Remote Sensing |
| Volume | 41 |
| Number | 5 |
| Pages | 1943-1969 |
| Secondary Mark | A1_PLANEJAMENTO_URBANO_E_REGIONAL_/_DEMOGRAFIA A2_INTERDISCIPLINAR A2_GEOGRAFIA A2_ENGENHARIAS_IV A2_ENGENHARIAS_III A2_ENGENHARIAS_I A2_CIÊNCIAS_AMBIENTAIS A2_CIÊNCIA_DA_COMPUTAÇÃO B1_MATEMÁTICA_/_PROBABILIDADE_E_ESTATÍSTICA B1_GEOCIÊNCIAS B1_ENGENHARIAS_II B1_CIÊNCIAS_AGRÁRIAS_I B1_BIODIVERSIDADE B2_SAÚDE_COLETIVA B2_ODONTOLOGIA B3_CIÊNCIAS_BIOLÓGICAS_I B3_BIOTECNOLOGIA B5_ASTRONOMIA_/_FÍSICA |
| History (UTC) | 2019-12-03 15:22:37 :: lattes -> administrator :: 2019 2019-12-06 14:04:44 :: administrator -> lattes :: 2019 2019-12-06 14:06:21 :: lattes -> administrator :: 2019 2020-01-06 11:35:24 :: administrator -> simone :: 2019 2020-01-06 16:47:01 :: simone :: 2019 -> 2020 2020-01-06 16:47:02 :: simone -> administrator :: 2020 2022-01-04 01:31:11 :: administrator -> simone :: 2020 |
| 3. Content and structure | |
| Is the master or a copy? | is the master |
| Content Stage | completed |
| Transferable | 1 |
| Content Type | External Contribution |
| Version Type | publisher |
| Keywords | forest succession stages endangered tree species convolutional neural networks ensemble methods light detection and ranging multispectral data |
| Abstract | This work explores the integration of airborne Light Detection and Ranging (LiDAR) data and WorldView-2 (WV2) images to classify the land cover of a subtropical forest area in Southern Brazil. Different deep and machine learning methods were used: one based on convolutional neural network (CNN) and three ensemble methods. We adopted both pixel- (in the case of CNN) and object-based approaches. The results demonstrated that the integration of LiDAR and WV2 data led to a significant increase (7% to 16%) in accuracies for all classifiers, with kappa coefficient (κ) ranging from 0.74 for the random forest (RF) classifier associated with the WV2 dataset, to 0.92 for the forest by penalizing attributes (FPA) with the full (LiDAR + WV2) dataset. Using the WV2 dataset solely, the best κ was 0.81 with CNN classifier, while for the LiDAR dataset, the best κ was 0.8 with the rotation forest (RotF) algorithm. The use of LiDAR data was especially useful for the discrimination of vegetation classes because of the different height properties among them. In its turn, the WV2 data provided better performance for classes with less structure variation, such as field and bare soil. All the classification algorithms had a nearly similar performance: the results vary slightly according to the dataset used and none of the methods achieved the best accuracy for all classes. It was noticed that both datasets (WV2 and LiDAR) even when applied alone achieved good results with deep and machine learning methods. However, the advantages of integrating active and passive sensors were evident. All these methods provided promising results for land cover classification experiments of the study area in this work. |
| Area | SRE |
| Arrangement 1 | urlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDSR > A comparison of... |
| Arrangement 2 | urlib.net > BDMCI > Fonds > Produção pgr ATUAIS > SER > A comparison of... |
| doc Directory Content | access |
| source Directory Content | there are no files |
| agreement Directory Content | there are no files |
| 4. Conditions of access and use | |
| Language | en |
| Target File | sothe_comparison.pdf |
| User Group | self-uploading-INPE-MCTI-GOV-BR |
| Reader Group | administrator simone |
| Visibility | shown |
| Archiving Policy | denypublisher denyfinaldraft12 |
| Read Permission | deny from all and allow from 150.163 |
| Update Permission | not transferred |
| 5. Allied materials | |
| Next Higher Units | 8JMKD3MGPCW/3ER446E 8JMKD3MGPCW/3F3NU5S |
| Citing Item List | sid.inpe.br/bibdigital/2013/10.18.22.34 2 sid.inpe.br/bibdigital/2013/09.13.21.11 1 |
| URL (untrusted data) | https://www.tandfonline.com/doi/full/10.1080/01431161.2019.1681600 |
| Dissemination | WEBSCI; PORTALCAPES; COMPENDEX; SCOPUS. |
| Host Collection | dpi.inpe.br/plutao@80/2008/08.19.15.01 |
| 6. Notes | |
| Notes | Setores de Atividade: Atividades dos serviços de tecnologia da informação, Pesquisa e desenvolvimento científico, Produção Florestal. |
| Empty Fields | alternatejournal archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress format isbn lineage mark mirrorrepository month nextedition orcid parameterlist parentrepositories previousedition previouslowerunit progress project rightsholder schedulinginformation secondarydate secondarykey session shorttitle sponsor subject tertiarymark tertiarytype typeofwork |
| 7. Description control | |
| e-Mail (login) | simone |
| update | |