| 1. Identity statement | ||||
| Reference Type | Journal Article | |||
| Site | mtc-m21c.sid.inpe.br | |||
| Holder Code | isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S | |||
| Identifier | 8JMKD3MGP3W34R/43T9E9E | |||
| Repository | sid.inpe.br/mtc-m21c/2021/01.05.14.44 (restricted access) | |||
| Last Update | 2021:01.05.14.44.08 (UTC) simone | |||
| Metadata Repository | sid.inpe.br/mtc-m21c/2021/01.05.14.44.08 | |||
| Metadata Last Update | 2022:04.03.22.28.00 (UTC) administrator | |||
| DOI | 10.1016/j.isprsjprs.2020.11.007 | |||
| ISSN | 0924-2716 | |||
| Citation Key | MartinezLaRFeiSanHap:2021:FuCoRe | |||
| Title | Fully convolutional recurrent networks for multidate crop recognition from multitemporal image sequences  | |||
| Year | 2021 | |||
| Month | Jan. | |||
| Access Date | 2024, May 19 | |||
| Type of Work | journal article | |||
| Secondary Type | PRE PI | |||
| Number of Files | 1 | |||
| Size | 4667 KiB | |||
| 2. Context | ||||
| Author | 1 Martinez, Jorge Andres Chamorro 2 La Rosa, Laura Elena Cué 3 Feitosa, Raul Queiroz 4 Sanches, Ieda Del'Arco 5 Happ, Patrick Nigri | |||
| Group | 1 2 3 4 DIOTG-CGCT-INPE-MCTI-GOV-BR | |||
| Affiliation | 1 Pontificia Universidade Católica do Rio de Janeiro (PUC-Rio) 2 Pontificia Universidade Católica do Rio de Janeiro (PUC-Rio) 3 Pontificia Universidade Católica do Rio de Janeiro (PUC-Rio) 4 Instituto Nacional de Pesquisas Espaciais (INPE) 5 Pontificia Universidade Católica do Rio de Janeiro (PUC-Rio) | |||
| Author e-Mail Address | 1 jchamorro@ele.puc-rio.br 2 lauracue@ele.puc-rio.br 3 raul@ele.puc-rio.br 4 ieda.sanches@inpe.br 5 patrick@ele.puc-rio.br | |||
| Journal | ISPRS Journal of Photogrammetry and Remote Sensing | |||
| Volume | 171 | |||
| Pages | 188-201 | |||
| Secondary Mark | A1_GEOCIÊNCIAS A2_INTERDISCIPLINAR A2_CIÊNCIAS_AMBIENTAIS B1_ENGENHARIAS_IV B1_BIODIVERSIDADE C_CIÊNCIAS_AGRÁRIAS_I | |||
| History (UTC) | 2021-01-05 14:44:09 :: simone -> administrator :: 2021-01-05 14:44:10 :: administrator -> simone :: 2021 2021-01-05 14:45:19 :: simone -> administrator :: 2021 2022-04-03 22:28:00 :: administrator -> simone :: 2021 | |||
| 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 | Convolutional recurrent networks Fully convolutional networks Recurrent networks Crop recognition Deep learning Remote sensing | |||
| Abstract | Crop recognition in tropical regions is a challenging task because of the highly complex crop dynamics, with multiple crops per year. Nevertheless, most automatic methods proposed thus far are devoted to temperate areas where normally a single crop is cultivated along the crop year. This paper introduces convolutional recurrent networks for crop recognition in areas characterized by complex spatiotemporal dynamics typical of tropical agriculture, where a per date classification is required. The proposed networks consist of two sequential steps. First, a deep network simultaneously models spatial and temporal contexts. Second, a post-processing algorithm enforces prior knowledge about the crop dynamics in the target area based on the posterior probabilities computed in the first step. The paper proposes deep network architectures that join a fully convolutional network (FCN) for modeling spatial context at multiple levels and a bidirectional recurrent neural network to explore the temporal context. The recurrent network is configured as N-to-N, where N is the sequence length. This allows it to produce classification outcomes for the entire sequence of multi-temporal images using a single network. Different network designs are proposed based on three FCN architectures: U-Net, dense network, and Atrous Spatial Pyramid Pooling. A convolutional Long-Short-Term-Memory (ConvLSTM) accounts for sequence modeling, whereas the Most Likely Class Sequence (MLCS) algorithm is adopted for enforcing prior knowledge. The paper finally reports experiments conducted on Sentinel-1 data of two publicly available datasets from different tropical regions. The experimental results indicated that the proposed architectures outperformed state-of-the-art methods based on recurrent networks in terms of Overall Accuracy and per-class F1 score. | |||
| Area | SRE | |||
| Arrangement | urlib.net > BDMCI > Fonds > Produção a partir de 2021 > CGCT > Fully convolutional recurrent... | |||
| 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 | |||
| Target File | martinez_fully.pdf | |||
| User Group | simone | |||
| Reader Group | administrator simone | |||
| Visibility | shown | |||
| Archiving Policy | denypublisher denyfinaldraft24 | |||
| Read Permission | deny from all and allow from 150.163 | |||
| Update Permission | not transferred | |||
| 5. Allied materials | ||||
| Next Higher Units | 8JMKD3MGPCW/46KUATE | |||
| Dissemination | WEBSCI; PORTALCAPES; COMPENDEX; SCOPUS. | |||
| Host Collection | urlib.net/www/2017/11.22.19.04 | |||
| 6. Notes | ||||
| Empty Fields | alternatejournal archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress format isbn label lineage mark mirrorrepository nextedition notes number orcid parameterlist parentrepositories previousedition previouslowerunit progress project resumeid rightsholder schedulinginformation secondarydate secondarykey session shorttitle sponsor subject tertiarymark tertiarytype url | |||
| 7. Description control | ||||
| e-Mail (login) | simone | |||
| update | ||||