| Texto completo | |
| Autor(es): |
Carvalho, Thiago
;
Martinez, Jorge A. Chamorro
;
Oliveira, Hugo
;
dos Santos, Jefersson A.
;
Feitosa, Raul Queiroz
Número total de Autores: 5
|
| Tipo de documento: | Artigo Científico |
| Fonte: | IEEE Geoscience and Remote Sensing Letters; v. 20, p. 5-pg., 2023-01-01. |
| Resumo | |
When it comes to technology in agriculture, one of the most important aspects is farmland crop monitoring. However, in most cases, only the main crops are needed to be monitored by satellite images, due to their high territorial extension. Therefore, a semantic segmentation model for identifying plantations should correctly classify the majority classes and also automatically identify other unknown crops. Open set recognition (OSR) aims to embrace both of these causes, so that the model can be more robust in the wild. This work adapts the framework of outlier exposure (OE) for open set image segmentation. OE was evaluated by adding it to three distinct methods for open set segmentation: softmax thresholding, OpenPCS and OpenPCS++. We conducted several experiments to enrich the discussion of the impact of OE on the semantic segmentation of crop imagery. Our methodology achieved a consistent increase for OpenPCS and OpenPCS++ methods, with an improvement of up to 7.5% in terms of area under the receiver operating characteristic (AUROC) curve if compared to previous work. (AU) | |
| 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 |