| 1. Identity statement | ||||
| Reference Type | Journal Article | |||
| Site | mtc-m21b.sid.inpe.br | |||
| Holder Code | isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S | |||
| Identifier | 8JMKD3MGP3W34P/3LKJAS2 | |||
| Repository | sid.inpe.br/mtc-m21b/2016/05.03.16.12 (restricted access) | |||
| Last Update | 2017:07.21.16.44.19 (UTC) simone | |||
| Metadata Repository | sid.inpe.br/mtc-m21b/2016/05.03.16.12.24 | |||
| Metadata Last Update | 2018:06.04.02.40.45 (UTC) administrator | |||
| DOI | 10.1080/01431161.2016.1165883 | |||
| ISSN | 0143-1161 | |||
| Citation Key | NegriDutrSantLu:2016:ExReMe | |||
| Title | Examining region-based methods for land cover classification using stochastic distances  | |||
| Year | 2016 | |||
| Month | Apr. | |||
| Access Date | 2024, Apr. 28 | |||
| Type of Work | journal article | |||
| Secondary Type | PRE PI | |||
| Number of Files | 1 | |||
| Size | 3830 KiB | |||
| 2. Context | ||||
| Author | 1 Negri, Rogério G. 2 Dutra, Luciano Vieira 3 Sant'Anna, Sidnei João Siqueira 4 Lu, D. | |||
| Resume Identifier | 1 2 8JMKD3MGP5W/3C9JHMA 3 8JMKD3MGP5W/3C9JJ8N | |||
| Group | 1 2 DPI-OBT-INPE-MCTI-GOV-BR 3 DPI-OBT-INPE-MCTI-GOV-BR | |||
| Affiliation | 1 Universidade Estadual Paulista (UNESP) 2 Instituto Nacional de Pesquisas Espaciais (INPE) 3 Instituto Nacional de Pesquisas Espaciais (INPE) 4 Michigan State University | |||
| Author e-Mail Address | 1 rogerio.negri@ict.unesp.br 2 luciano.dutra@inpe.br 3 sidnei.santanna@inpe.br | |||
| Journal | International Journal of Remote Sensing | |||
| Volume | 37 | |||
| Number | 8 | |||
| Pages | 1902-1921 | |||
| 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) | 2016-05-03 16:12:24 :: simone -> administrator :: 2017-01-09 13:59:27 :: administrator -> simone :: 2016 2017-07-21 16:44:19 :: simone -> administrator :: 2016 2018-06-04 02:40:45 :: administrator -> simone :: 2016 | |||
| 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 | Graph theory Pixels Radar imaging Remote sensing Stochastic systems Support vector machines Synthetic aperture radar | |||
| Abstract | A recent alternative to standard pixel-based classification of remote-sensing data is region-based classification, which has proved to be particularly useful when analysing high-resolution imagery of complex environments, such as urban areas, or when addressing noisy data, such as synthetic aperture radar (SAR) images. First, following certain criteria, the imagery is decomposed into homogeneous regions, and then each region is classified into a class of interest. The usual method for region-based classification involves using stochastic distances, which measure the distances between the pixel distributions inside an unknown region and the representative distributions of each class. The class, which is at the minimum distance from the unknown region distribution, is assigned to the region and this procedure is termed stochastic minimum distance classification (SMDC). This study reports the use of methods derived from the original SMDC, Support Vector Machine (SVM), and graph theory, with the objective of identifying the most robust and accurate classification methods. The equivalent pixel-based versions of region-based analysed methods were included for comparison. A case study near the Tapajós National Forest, in Pará state, Brazil, was investigated using ALOS PALSAR data. This study showed that methods based on the nearest neighbour, derived from SMDC, and SVM, with a specific kernel function, are more accurate and robust than the other analysed methods for region-based classification. Furthermore, pixel-based methods are not indicated to perform the classification of images with a strong presence of noise, such as SAR images. | |||
| Area | SRE | |||
| Arrangement | urlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDPI > Examining region-based methods... | |||
| doc Directory Content | access | |||
| source Directory Content | there are no files | |||
| agreement Directory Content |
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| 4. Conditions of access and use | ||||
| Language | en | |||
| User Group | simone | |||
| 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 | ||||
| Mirror Repository | urlib.net/www/2011/03.29.20.55 | |||
| Next Higher Units | 8JMKD3MGPCW/3EQCCU5 | |||
| Citing Item List | sid.inpe.br/bibdigital/2013/09.09.15.05 3 sid.inpe.br/mtc-m21/2012/07.13.15.00.20 3 sid.inpe.br/mtc-m21/2012/07.13.14.53.50 2 | |||
| Dissemination | WEBSCI; PORTALCAPES; COMPENDEX; SCOPUS. | |||
| Host Collection | sid.inpe.br/mtc-m21b/2013/09.26.14.25.20 | |||
| 6. Notes | ||||
| Empty Fields | alternatejournal archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress format isbn label lineage mark nextedition notes orcid parameterlist parentrepositories previousedition previouslowerunit progress project rightsholder schedulinginformation secondarydate secondarykey session shorttitle sponsor subject targetfile tertiarymark tertiarytype url | |||
| 7. Description control | ||||
| e-Mail (login) | simone | |||
| update | ||||