| Texto completo | |
| Autor(es): |
Chamorro Martinez, Jorge A.
[1]
;
Oliveira, Hugo
[2]
;
dos Santos, Jefersson A.
[3]
;
Feitosa, Raul Queiroz
[1]
Número total de Autores: 4
|
| Afiliação do(s) autor(es): | [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
Número total de Afiliações: 3
|
| Tipo de documento: | Artigo Científico |
| Fonte: | IEEE Geoscience and Remote Sensing Letters; v. 19, 2022. |
| Citações Web of Science: | 0 |
| Resumo | |
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 https://github.com/DiMorten/osss-mcr. (AU) | |
| Processo FAPESP: | 17/50236-1 - Análise espaço-temporal de imagens de ressonância magnética |
| Beneficiário: | Roberto Marcondes Cesar Junior |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |
| Processo FAPESP: | 20/06744-5 - Deep learning e representações intermediárias para análise de imagens pediátricas |
| Beneficiário: | Hugo Neves de Oliveira |
| Modalidade de apoio: | Bolsas no Brasil - Pós-Doutorado |
| Processo FAPESP: | 15/22308-2 - Representações intermediárias em Ciência Computacional para descoberta de conhecimento |
| Beneficiário: | Roberto Marcondes Cesar Junior |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |