Semi-supervised methods for CNN based classification of multispectral imagery

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.