Towards vegetation species discrimination by using data-driven descriptors

• Autor(a) principal: Nogueira, Keiller
• Data de Publicação: 2016
• Outros Autores: Santos, Jefersson A. dos, Fornazari, Tamires [UNESP], Freire Silva, Thiago Sanna [UNESP], Morellato, Leonor Patricia [UNESP], Torres, Ricardo da S., IEEE
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://hdl.handle.net/11449/165616
• topic: Deep Learning; Remote Sensing; Feature Learning; Image Classification; Machine Learning; High-resolution Images

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.