Advanced search
Start date
(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Open Set Semantic Segmentation for Multitemporal Crop Recognition

Full text
Chamorro Martinez, Jorge A. [1] ; Oliveira, Hugo [2] ; dos Santos, Jefersson A. [3] ; Feitosa, Raul Queiroz [1]
Total Authors: 4
[1] Pontifical Catholic Univ Rio de Janeiro, Dept Elect Engn, BR-22541041 Rio De Janeiro - Brazil
[2] Univ Sao Paulo, Inst Math & Stat IME, BR-05508220 Sao Paulo - Brazil
[3] Univ Fed Minas Gerais UFMG, Dept Comp Sci DCC, BR-31270901 Belo Horizonte, MG - Brazil
Total Affiliations: 3
Document type: Journal article
Source: IEEE Geoscience and Remote Sensing Letters; v. 19, 2022.
Web of Science Citations: 0

Multitemporal remote-sensing images play a key role as a source of information for automated crop mapping and monitoring. The spatial/spectral pattern evolution along time provides information about the dynamics of the crops and are very useful for productivity estimation. Although the multitemporal mapping of crops has progressed considerably with the advent of deep learning in recent years, the classification models obtained still have limitations when exposed to unknown classes in the prediction phase, reducing their usefulness. In other words, these models are trained to identify a closed set of crops (e.g., soy and sugar cane) and are therefore unable to recognize other types of crops (e.g., maize). In this letter, we deal with the challenges of multitemporal crop recognition by proposing a new approach called OpenPCS++ that is not only able to learn known classes but is also capable of identifying new crops in the predicting phase. The proposed approach was evaluated in two challenging public datasets located in tropical climates in Brazil. Results showed that OpenPCS++ achieved increases of up to 0.19 in terms of area under the receiver-operating characteristic (ROC) curve in comparison with baselines. Code is available at (AU)

FAPESP's process: 17/50236-1 - Spatio temporal analysis of pediatric magnetic resonance images
Grantee:Roberto Marcondes Cesar Junior
Support Opportunities: Regular Research Grants
FAPESP's process: 20/06744-5 - Deep learning and intermediate representations for pediatric image analysis
Grantee:Hugo Neves de Oliveira
Support Opportunities: Scholarships in Brazil - Post-Doctoral
FAPESP's process: 15/22308-2 - Intermediate representations in Computational Science for knowledge discovery
Grantee:Roberto Marcondes Cesar Junior
Support Opportunities: Research Projects - Thematic Grants