Satellite image classification plays a critical role in numerous applications, from environmental monitoring to urban planning. Traditional deep learning models, such as Convolutional Neural Networks (CNNs), ResNet, and U-Net, rely heavily on large amounts of labeled data, which can be difficult and expensive to obtain at scale. To address this challenge, we explore the use of semi-supervised learning (SSL) for satellite image classification. In this study, we implemented FixMatch, a cutting-edge SSL technique, on the EuroSAT dataset, consisting of 27,000 images across 10 classes. FixMatch leverages both labeled and unlabeled data by combining consistency regularization with pseudo-labeling. The threshold is a crucial factor in generating pseudo-labels, while the ratio of labeled to unlabeled data plays a significant role in FixMatch’s performance. Therefore, we conduct extensive analyses on various ratios of labeled to unlabeled data and threshold values. Our implementation achieved a highest 97% classification accuracy within 30 min of training time. With only 5% labeled data, the model maintained a 94% accuracy in just 10 min, and at a 98% confidence threshold, it obtained 92% accuracy. In FixMatch, we integrated three pre-trained CNN models—ResNet50, GoogleNet, and VGG19—to train them on labeled data and build the teacher model to guide the unsupervised learning. Among these, the GoogleNet-integrated FixMatch model achieved the best classification accuracy and the shortest training time. Our study demonstrates the effectiveness of FixMatch in enhancing satellite image classification performance using minimal labeled data with reduced training time. By leveraging a small set of labeled data along with a larger volume of unlabeled data, FixMatch enables faster and more cost-effective model training and significantly reduce the reliance on extensive labeling efforts.