Multispectral Data

Multispectral data, encompassing images with multiple spectral bands beyond the visible spectrum, is increasingly used to extract detailed information across diverse fields, from remote sensing of Earth's surface to astronomical imaging. Current research focuses on improving data fusion techniques from multiple sensors and spectral resolutions, often employing deep learning architectures like convolutional neural networks (CNNs), transformers, and masked autoencoders (MAEs) to analyze complex spatial and temporal patterns. These advancements enable more accurate and efficient applications in areas such as precision agriculture, wildfire detection, environmental monitoring (e.g., methane emissions, soil organic carbon), and urban planning, ultimately leading to improved data analysis and decision-making.