Automatic Labeling of Remote Sensing Data fastens analysis in various applications such as environmental monitoring, urban planning, and disaster management. Supervised machine learning approaches rely on labeled datasets created through time-consuming processes. Creation of labeled datasets requires higher resources and such datasets are harder to obtain in most of the domains, and especially in Remote Sensing. This study proposes Cross-Model Self-Supervised Feature Extraction (CMSFE), a novel approach that enhances representation learning in unlabeled remote sensing datasets by integrating features from multiple pre-trained models and refining them through self-supervised learning (SSL). The extracted features are integrated to form a comprehensive and robust feature set that aids in separating different cluster of imagery. Experimental results with EuroSAT dataset demonstrate the quality of feature extraction in separating various classes without any manual intervention or labeling. Dimensionality Reduction and Manifold Learning is applied for visual interpretation of extracted feature space. These features can be further reused for analysis or modeling, highlighting the potential of SSL-based feature extraction methods in remote sensing to enhance representation learning and reduce dependency on labeled data.