Environmental monitoring using drone images and convolutional neural networks

• Autor(a) principal: Thomazella, R.
• Data de Publicação: 2018
• Outros Autores: Castanho, J. E., Dotto, F. R.L., Rodrigues Júnior, O. P., Rosa, G. H., Marana, A. N., Papa, J. P.
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1109/IGARSS.2018.8518581; http://hdl.handle.net/11449/231431
• topic: Convolutional Neural Networks; Drones; Land-use classification

Recently, drone images have been used in a number of applications, mainly for pollution control and surveillance purposes. In this paper, we introduce the well-known Convolutional Neural Networks in the context of environmental monitoring using drone images, and we show their robustness in real-world images obtained from uncontrolled scenarios. We consider a transfer learning-based approach and compare two neural models, i.e., VGG16 and VGG19, to distinguish four classes: water, deforesting area, forest, and buildings. The results are analyzed by experts in the field and considered pretty much reasonable.