Remote Sensing Pansharpening

Pansharpening is an image fusion technique that combines high-resolution panchromatic (PAN) and low-resolution multispectral (MS) satellite imagery to create high-resolution MS images with improved spatial detail and spectral information. Current research emphasizes developing advanced deep learning models, including convolutional neural networks (CNNs), transformers, and diffusion models, often incorporating novel loss functions and attention mechanisms to optimize both spatial and spectral fidelity. These advancements are crucial for enhancing the accuracy and utility of remote sensing data in various applications, such as land cover classification, environmental monitoring, and archaeological prospection, by providing higher-resolution imagery for analysis. Ongoing work also focuses on improving the evaluation and validation of pansharpening methods, addressing challenges in establishing reliable ground truth data.