  Semi-supervised learning (SSL) has made significant strides in the field of
remote sensing. Finding a large number of labeled datasets for SSL methods is
uncommon, and manually labeling datasets is expensive and time-consuming.
Furthermore, accurately identifying remote sensing satellite images is more
complicated than it is for conventional images. Class-imbalanced datasets are
another prevalent phenomenon, and models trained on these become biased towards
the majority classes. This becomes a critical issue with an SSL model's subpar
performance. We aim to address the issue of labeling unlabeled data and also
solve the model bias problem due to imbalanced datasets while achieving better
accuracy. To accomplish this, we create "artificial" labels and train a model
to have reasonable accuracy. We iteratively redistribute the classes through
resampling using a distribution alignment technique. We use a variety of class
imbalanced satellite image datasets: EuroSAT, UCM, and WHU-RS19. On UCM
balanced dataset, our method outperforms previous methods MSMatch and FixMatch
by 1.21% and 0.6%, respectively. For imbalanced EuroSAT, our method outperforms
MSMatch and FixMatch by 1.08% and 1%, respectively. Our approach significantly
lessens the requirement for labeled data, consistently outperforms alternative
approaches, and resolves the issue of model bias caused by class imbalance in
datasets.
