Towards vegetation species discrimination by using data-driven descriptors

Autor(es)

Nogueira, Keiller ; dos Santos, Jefersson A. ; Fornazari, Tamires ; Freire Silva, Thiago Sanna ; Morellato, Leonor Patricia ; Torres, Ricardo da S. ; IEEE

• Tipo de documento: Artigo Científico
• Fonte: 2016 9TH IAPR WORKSHOP ON PATTERN RECOGNITION IN REMOTE SENSING (PRRS); v. N/A, p. 6-pg., 2016-01-01.

Resumo

In this paper, we analyse the use of Convolutional Neural Networks (CNNs or ConvNets) to discriminate vegetation species with few labelled samples. To the best of our knowledge, this is the first work dedicated to the investigation of the use of deep features in such task. The experimental evaluation demonstrate that deep features significantly outperform well-known feature extraction techniques. The achieved results also show that it is possible to learn and classify vegetation patterns even with few samples. This makes the use of our approach feasible for real-world mapping applications, where it is often difficult to obtain large training sets. (AU)

• Processo FAPESP: 13/50169-1 - Towards an understanding of tipping points within tropical South American biomes
• Beneficiário: Ricardo da Silva Torres
• Modalidade de apoio: Auxílio à Pesquisa - Parceria para Inovação Tecnológica - PITE
• Processo FAPESP: 13/50155-0 - Combining new technologies to monitor phenology from leaves to ecosystems
• Beneficiário: Leonor Patricia Cerdeira Morellato
• Modalidade de apoio: Auxílio à Pesquisa - Programa de Pesquisa sobre Mudanças Climáticas Globais - PITE