Multispectral Satellite

Multispectral satellite imagery, capturing data across multiple wavelengths, is revolutionizing Earth observation by enabling detailed analysis of diverse features. Current research focuses on improving data processing and analysis using deep learning models like convolutional neural networks (CNNs) and Vision Transformers (ViTs), addressing challenges such as cloud cover imputation, automated feature extraction (e.g., linear disturbances, solar farms), and improved classification accuracy through techniques like kernel optimization and cross-modality fusion. These advancements significantly enhance applications ranging from environmental monitoring (wildfires, deforestation, marine debris) to resource management and disaster response, providing valuable data for scientific research and practical decision-making.