A Comprehensive Framework for Improving Remote Sensing Image Classification: Combining Augmentation and Missing Pixel Imputation

Remote sensing image classification is crucial in various domains including agriculture, urban planning, and environmental monitoring. However, limited labeled data and missing pixels pose challenges to achieving accurate classification. In this study, we propose a comprehensive framework that integrates data augmentation using a latent diffusion model and reinforcement learning-based missing pixel imputation to enhance deep learning models' classification performance. The framework consists of three layers: data augmentation, missing pixel imputation, and classification using a modified VGG16 architecture. Extensive experiments on benchmark datasets demonstrate the significant impact of our framework, surpassing state-of-the-art techniques by significantly improving classification accuracy and robustness. The results highlight the effectiveness of our augmentation and imputation techniques, achieving remarkable Dice Score, Accuracy, and Recall metrics of 97.56%, 97.34%, and 97.34%, respectively. Our proposed framework provides a valuable solution for accurate remote sensing image classification, addressing the challenges of limited data and missing pixels, and has broad applications in various domains.