Source: International Journal of Remote Sensing. Unidade: ESALQ
Subjects: ESPECTROSCOPIA, ECOSSISTEMAS FLORESTAIS, IMAGEAMENTO DE SATÉLITE, PAISAGEM, FLORESTAS TROPICAIS, REFLORESTAMENTO
ABNT
FERREIRA, Matheus Pinheiro et al. Exploring EnMAP hyperspectral images and ensemble deep learning for classifying forest land-cover types in Brazil. International Journal of Remote Sensing, p. 1-26, 2026Tradução . . Disponível em: https://doi.org/10.1080/01431161.2026.2628300. Acesso em: 22 abr. 2026.APA
Ferreira, M. P., Fuza, M. S., Viveiros, J. M. S. M., Ferranti, T., Oliveira, D., Santoro, G. B., et al. (2026). Exploring EnMAP hyperspectral images and ensemble deep learning for classifying forest land-cover types in Brazil. International Journal of Remote Sensing, 1-26. doi:10.1080/01431161.2026.2628300NLM
Ferreira MP, Fuza MS, Viveiros JMSM, Ferranti T, Oliveira D, Santoro GB, Molin PG, Almeida CT, Resende AF, Almeida DRA de, Zeng Y, Rodrigues RR, Brancalion PHS. Exploring EnMAP hyperspectral images and ensemble deep learning for classifying forest land-cover types in Brazil [Internet]. International Journal of Remote Sensing. 2026 ; 1-26.[citado 2026 abr. 22 ] Available from: https://doi.org/10.1080/01431161.2026.2628300Vancouver
Ferreira MP, Fuza MS, Viveiros JMSM, Ferranti T, Oliveira D, Santoro GB, Molin PG, Almeida CT, Resende AF, Almeida DRA de, Zeng Y, Rodrigues RR, Brancalion PHS. Exploring EnMAP hyperspectral images and ensemble deep learning for classifying forest land-cover types in Brazil [Internet]. International Journal of Remote Sensing. 2026 ; 1-26.[citado 2026 abr. 22 ] Available from: https://doi.org/10.1080/01431161.2026.2628300
