ENVIRONMENTAL MONITORING USING DRONE IMAGES AND CONVOLUTIONAL NEURAL NETWORKS
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Data
2018-01-01
Autores
Thomazella, R. [UNESP]
Castanho, J. E. [UNESP]
Dotto, F. R. L. [UNESP]
Rodrigues Junior, O. P.
Rosa, G. H. [UNESP]
Marana, A. N. [UNESP]
Papa, J. P. [UNESP]
IEEE
Título da Revista
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Editor
Ieee
Resumo
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.
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Palavras-chave
Land-use classification, Drones, Convolutional Neural Networks
Como citar
Igarss 2018 - 2018 Ieee International Geoscience And Remote Sensing Symposium. New York: Ieee, p. 8941-8944, 2018.