| Texto completo | |
| Autor(es): |
Faria, Fabio A.
;
Buris, Luiz H.
;
Pereira, Luis A. M.
;
Cappabianco, Fabio A. M.
Número total de Autores: 4
|
| Tipo de documento: | Artigo Científico |
| Fonte: | PROCEEDINGS OF THE 2024 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, GECCO 2024; v. N/A, p. 9-pg., 2024-01-01. |
| Resumo | |
Aerial scene classification in remote sensing presents a significant challenge due to high intra-class variability and the different scales and orientations of the objects within dataset images. While deep learning architectures are commonly used for scene classification tasks and in the remote sensing area, Deep metric learning (DML) offers a more adaptable solution for more challenging classification scenarios by learning the characteristics of each class. This study exploits the usage of DML approaches for aerial scene classification tasks, analyzing their behavior with different pre-trained Convolutional Neural Networks (CNNs), and their combination through evolutionary computation algorithms. Our experiments show that DML approaches can achieve better classification results as compared to traditional pre-trained CNNs for three well-known remote sensing aerial scene datasets. Furthermore, we found that using the Univariate Marginal Distribution Algorithm (UMDA) to construct the final ensemble of varied DML-based classifiers is essential for achieving consistency and high-accuracy results across all datasets, improving the state-of-the-art by over 5.6%. (AU) | |
| Processo FAPESP: | 21/01870-5 - Métodos de Fusão de Representações Multi-nível baseado em Aprendizagem Fracamente Supervisionada. |
| Beneficiário: | Luiz Henrique Buris |
| Modalidade de apoio: | Bolsas no Brasil - Mestrado |
| Processo FAPESP: | 17/25908-6 - Aprendizado fracamente supervisionado para análise de vídeos no domínio comprimido em tarefas de recuperação e classificação para alertas visuais |
| Beneficiário: | João Paulo Papa |
| Modalidade de apoio: | Auxílio à Pesquisa - Parceria para Inovação Tecnológica - PITE |
| Processo FAPESP: | 18/23908-1 - Buscando Robustez em Arquiteturas de Aprendizagem Profunda para Aplicações e-Science |
| Beneficiário: | Fabio Augusto Faria |
| Modalidade de apoio: | Bolsas no Exterior - Pesquisa |