Towards vegetation species discrimination by using data-driven descriptors
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Date
Advisor
Coadvisor
Graduate program
Undergraduate course
Journal Title
Journal ISSN
Volume Title
Publisher
Ieee
Type
Work presented at event
Access right
Acesso aberto
Abstract
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.
Description
Keywords
Deep Learning, Remote Sensing, Feature Learning, Image Classification, Machine Learning, High-resolution Images
Language
English
Citation
2016 9th Iapr Workshop On Pattern Recognition In Remote Sensing (prrs). New York: Ieee, 6 p., 2016.
