Review of Noise Removal in Passive Remote Sensing: Comparative MSI vs HSI Perspectives with Indian Case Studies

The proliferation of passive remote sensing (RS) missions particularly multispectral (MSI) and hyperspectral (HSI) sensors has enabled wide-ranging applications in agriculture, water resources, urban monitoring, and climate studies. However, the presence of noise arising from atmospheric effects, sensor calibration errors, striping, Gaussian disturbances, and mixed pixels significantly degrades image quality, limiting the accuracy of downstream tasks such as feature extraction, classification, and spectral unmixing. While substantial research has focused on classification and dimensionality reduction, comparatively fewer efforts have systematically reviewed noise mitigation strategies. This review provides a comprehensive analysis of noise removal techniques in passive RS, spanning statistical filtering, band selection, ensemble learning, and advanced machine learning (ML) and deep learning (DL) approaches, including recent transformer-based architectures. We emphasize the comparative impact of noise in MSI versus HSI data as well as highlight Indian case studies with open datasets (Resourcesat, Cartosat, HySIS, Sentinel-2), and analyze emerging trends such as semi-supervised learning and multimodal fusion. By integrating classical methods with modern ML pipelines, this review offers a structured perspective on challenges, progress, and future research directions for robust and noise-resilient passive RS applications