  Supervised learning techniques are at the center of many tasks in remote
sensing. Unfortunately, these methods, especially recent deep learning methods,
often require large amounts of labeled data for training. Even though
satellites acquire large amounts of data, labeling the data is often tedious,
expensive and requires expert knowledge. Hence, improved methods that require
fewer labeled samples are needed. We present MSMatch, the first semi-supervised
learning approach competitive with supervised methods on scene classification
on the EuroSAT benchmark dataset. We test both RGB and multispectral images and
perform various ablation studies to identify the critical parts of the model.
The trained neural network achieves state-of-the-art results on EuroSAT with an
accuracy that is between 1.98% and 19.76% better than previous methods
depending on the number of labeled training examples. With just five labeled
examples per class we reach 94.53% and 95.86% accuracy on the EuroSAT RGB and
multispectral datasets, respectively. With 50 labels per class we reach 97.62%
and 98.23% accuracy. Our results show that MSMatch is capable of greatly
reducing the requirements for labeled data. It translates well to multispectral
data and should enable various applications that are currently infeasible due
to a lack of labeled data. We provide the source code of MSMatch online to
enable easy reproduction and quick adoption.
