High Resolution Multispectral

High-resolution multispectral imaging focuses on acquiring and processing images with both fine spatial detail and multiple spectral bands, enabling detailed analysis of Earth's surface features. Current research emphasizes improving the fusion of multispectral and hyperspectral data, often employing deep learning models like convolutional neural networks, diffusion models, and graph neural networks to enhance resolution and accuracy in applications such as land cover classification and pansharpening. These advancements are significantly impacting various fields, including precision agriculture, urban planning, and environmental monitoring, by providing more accurate and detailed information from remote sensing data.