Semi-supervised Classification for Remote Sensing Datasets

Deep semi-supervised learning (DSSL) is a rapidly-growing field that takes advantage of a limited number of labeled examples to leverage massive amounts of unlabeled data. The underlying idea is that training on small yet well-selected examples can perform as effectively as a predictor trained on a larger number chosen at random [14]. In this study, we explore the most relevant approaches in DSSL literature like FixMatch [19], CoMatch [13], and, the class aware contrastive SSL (CCSSL) [25]. Our objective is to perform an initial comparative study of these methods and assess them on two remote sensing (RS) datasets: UCM [27] and AID [22]. The performance of these methods was determined based on their accuracy in comparison to a supervised benchmark. The results highlight that the CoMatch framework achieves the highest accuracy for both the UCM and AID datasets, with accuracies of 95.52% and 93.88% respectively. Importantly, all DSSL algorithms outperform the supervised benchmark, emphasizing their effectiveness in leveraging a limited number of labeled examples to enhance classification accuracy for remote sensing scene classification tasks. The code used in this study was adapted from CCSSL [25] and the detailed implementation will be accessible at https://github.com/itzahs/SSL-for-RS.