Semi-supervised GAN for Classification of Multispectral Imagery Acquired by UAVs
Unmanned aerial vehicles (UAV) are used in precision agriculture (PA) to enable aerial monitoring of farmlands. Intelligent methods are required to pinpoint weed infestations and make optimal choice of pesticide. UAV can fly a multispectral camera and collect data. However, the classification of mul...
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Main Authors | , , , |
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Format | Journal Article |
Language | English |
Published |
24.05.2019
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Subjects | |
Online Access | Get full text |
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Summary: | Unmanned aerial vehicles (UAV) are used in precision agriculture (PA) to
enable aerial monitoring of farmlands. Intelligent methods are required to
pinpoint weed infestations and make optimal choice of pesticide. UAV can fly a
multispectral camera and collect data. However, the classification of
multispectral images using supervised machine learning algorithms such as
convolutional neural networks (CNN) requires large amount of training data.
This is a common drawback in deep learning we try to circumvent making use of a
semi-supervised generative adversarial networks (GAN), providing a pixel-wise
classification for all the acquired multispectral images. Our algorithm
consists of a generator network that provides photo-realistic images as extra
training data to a multi-class classifier, acting as a discriminator and
trained on small amounts of labeled data. The performance of the proposed
method is evaluated on the weedNet dataset consisting of multispectral crop and
weed images collected by a micro aerial vehicle (MAV). The results by the
proposed semi-supervised GAN achieves high classification accuracy and
demonstrates the potential of GAN-based methods for the challenging task of
multispectral image classification. |
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DOI: | 10.48550/arxiv.1905.10920 |