Abstract
Deep Convolutional neuronal networks, with their recent increase in performance, have become one of the standard techniques for RGB image classification. Due to a lack of large labeled datasets, this is not the case for multispectral image classification. To overcome this, we analyze the use of semi-supervised learning for the case of multispectral datasets. We use parameter reduction strategies to create small and efficient multispectral CNNs and combine these computationally efficient classifiers with semi-supervised learning methods. We choose the state-of-the-art semi-supervised methods MixMatch, ReMix-Match, FixMatch, and FlexMatch, to conduct experiments on the multispectral dataset EuroSAT. Additionally, we challenge this semi-supervised multispectral approach with a decreasing number of labeled images. We found that with only 15 labeled images per class, we can reach an accuracy above 80 %. If more labeled images are provided, the analyzed semi-supervised methods can even surpass basic supervised learning strategies.
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