| 1. Identity statement | |
| Reference Type | Journal Article |
| Site | mtc-m21b.sid.inpe.br |
| Holder Code | isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S |
| Identifier | 8JMKD3MGP3W34P/3HP89AQ |
| Repository | sid.inpe.br/mtc-m21b/2015/01.13.18.16.34 (restricted access) |
| Last Update | 2015:02.10.18.41.12 (UTC) administrator |
| Metadata Repository | sid.inpe.br/mtc-m21b/2015/01.13.18.16.35 |
| Metadata Last Update | 2018:06.04.03.04.44 (UTC) administrator |
| DOI | 10.1080/01431161.2014.980920 |
| ISSN | 0143-1161 |
| Label | scopus 2015-01 LuLiMorDutBat:2014:RoTeIm |
| Citation Key | LuLiMorDutBat:2014:RoTeIm |
| Title | The roles of textural images in improving land-cover classification in the Brazilian Amazon  |
| Year | 2014 |
| Access Date | 2024, May 19 |
| Type of Work | journal article |
| Secondary Type | PRE PI |
| Number of Files | 1 |
| Size | 2896 KiB |
| 2. Context | |
| Author | 1 Lu, D. 2 Li, G. 3 Moran, E. 4 Dutra, Luciano Vieira 5 Batistella, M. |
| Resume Identifier | 1 2 3 4 8JMKD3MGP5W/3C9JHMA |
| Group | 1 2 3 4 DPI-OBT-INPE-MCTI-GOV-BR |
| Affiliation | 1 Zhejiang Provincial Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration, School of Environmental & Resource Sciences, Zhejiang A&F UniversityHangzhou, Zhejiang Province, China; Center for Global Change and Earth Observations, Michigan State UniversityEast Lansing, MI, United States 2 Center for Global Change and Earth Observations, Michigan State UniversityEast Lansing, MI, United States 3 Center for Global Change and Earth Observations, Michigan State UniversityEast Lansing, MI, United States 4 Instituto Nacional de Pesquisas Espaciais (INPE) 5 Embrapa Satellite MonitoringCampinas, SP, Brazil |
| e-Mail Address | marcelo.pazos@inpe.br |
| Journal | International Journal of Remote Sensing |
| Volume | 35 |
| Number | 24 |
| Pages | 8188-8207 |
| Secondary Mark | A1_ENGENHARIAS_III A2_CIÊNCIA_DA_COMPUTAÇÃO A2_ENGENHARIAS_I A2_GEOGRAFIA A2_INTERDISCIPLINAR A2_CIÊNCIAS_AMBIENTAIS A2_ENGENHARIAS_III A2_ENGENHARIAS_IV B1_MATEMÁTICA_/_PROBABILIDADE_E B1_CIÊNCIAS_AGRÁRIAS_I B1_BIODIVERSIDADE B1_GEOCIÊNCIAS B1_ENGENHARIAS_II B2_ODONTOLOGIA B2_SAÚDE_COLETIVA B3_CIÊNCIAS_BIOLÓGICAS_I B3_BIOTECNOLOGIA B5_ASTRONOMIA_/_FÍSICA |
| History (UTC) | 2018-06-04 03:04:44 :: administrator -> marcelo.pazos@inpe.br :: 2014 |
| 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 | Maximum likelihood Pixels Satellites Synthetic aperture radar Textures Advanced land observing satellites Classification accuracy Correlation coefficient Grey-level co-occurrence matrixes Land-cover classification Landsat Thematic Mapper Maximum likelihood classifiers Phased array type l-band synthetic aperture radars Image texture |
| Abstract | Texture has long been recognized as valuable in improving land-cover classification, but how data from different sensors with varying spatial resolutions affect the selection of textural images is poorly understood. This research examines textural images from the Landsat Thematic Mapper (TM), ALOS (Advanced Land Observing Satellite) PALSAR (Phased Array type L-band Synthetic Aperture Radar), the SPOT (Satellite Pour l'Observation de la Terre) high-resolution geometric (HRG) instrument, and the QuickBird satellite, which have pixel sizes of 30, 12.5, 10/5, and 0.6 m, respectively, for land-cover classification in the Brazilian Amazon. GLCM (grey-level co-occurrence matrix)-based texture measures with various sizes of moving windows are used to extract textural images from the aforementioned sensor data. An index based on standard deviations and correlation coefficients is used to identify the best texture combination following separability analysis of land-cover types based on training sample plots. A maximum likelihood classifier is used to conduct the land-cover classification, and the results are evaluated using field survey data. This research shows the importance of textural images in improving land-cover classification, and the importance becomes more significant as the pixel size improved. It is also shown that texture is especially important in the case of the ALOS PALSAR and QuickBird data. Overall, textural images have less capability in distinguishing land-cover types than spectral signatures, especially for Landsat TM imagery, but incorporation of textures into radiometric data is valuable for improving land-cover classification. The classification accuracy can be improved by 5.2-13.4% as the pixel size changes from 30 to 0.6 m. |
| Area | SRE |
| Arrangement | urlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDPI > The roles 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 |
| User Group | administrator marcelo.pazos@inpe.br |
| Reader Group | administrator marcelo.pazos@inpe.br |
| Visibility | shown |
| Archiving Policy | denypublisher denyfinaldraft12 |
| Read Permission | deny from all and allow from 150.163 |
| Update Permission | not transferred |
| 5. Allied materials | |
| Linking | Trabalho não Vinculado à Tese/Dissertação |
| Next Higher Units | 8JMKD3MGPCW/3EQCCU5 |
| Citing Item List | sid.inpe.br/mtc-m21/2012/07.13.14.53.50 5 sid.inpe.br/bibdigital/2013/09.09.15.05 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 electronicmailaddress format isbn lineage mark mirrorrepository month nextedition notes orcid parameterlist parentrepositories previousedition previouslowerunit progress project rightsholder schedulinginformation secondarydate secondarykey session shorttitle sponsor subject targetfile tertiarytype url |
| 7. Description control | |
| e-Mail (login) | marcelo.pazos@inpe.br |
| update | |