Hyperspectral Pansharpening

Hyperspectral pansharpening aims to improve the spatial resolution of hyperspectral images by fusing them with higher-resolution panchromatic imagery, yielding richer data for various applications. Current research emphasizes developing efficient and accurate fusion methods, exploring both traditional variational optimization techniques and deep learning approaches like convolutional neural networks (CNNs) and diffusion models, often within unsupervised learning frameworks to address data scarcity. These advancements are crucial for enhancing the quality and utility of hyperspectral remote sensing data in fields such as environmental monitoring and urban planning.