Semi-supervised GAN for Classification of Multispectral Imagery Acquired by UAVs
This work addresses the need for efficient weed detection in agriculture using UAVs, but it is incremental as it applies an existing semi-supervised GAN approach to a specific domain.
The paper tackled the problem of classifying multispectral UAV imagery for precision agriculture, where supervised methods require large labeled datasets, by proposing a semi-supervised GAN that generates extra training data to improve classification accuracy on the weedNet dataset.
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.