Semi-supervised methods for CNN based classification of multispectral imagery
Manuel Bihler,
Jiachen Zhou,
Michael Heizmann
Kapitel/Beitrag aus dem Buch: Beyerer, J et al. 2023. OCM 2023 - 6th International Conference on Optical Characterization of Materials, March 22nd – 23rd, 2023, Karlsruhe, Germany : Conference Proceedings.
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 semisupervised
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.