3 accepted papers
Subspace clustering is a powerful unsupervised approach for hyperspectral image (HSI) analysis, but its high computational and memory costs limit scalability. Superpixel segmentation can improve efficiency by reducing the number of data points to process. However, existing superpixel-based methods u…
Snapshot compressive imaging (SCI) captures multispectral images (MSIs) using a single coded two-dimensional (2-D) measurement, but reconstructing high-fidelity MSIs from these compressed inputs remains a fundamentally ill-posed challenge. Recent diffusion-based methods improve quality but are limit
Few-shot learning (FSL) has been employed in hyperspectral image (HSI) classification, achieving excellent performance with limited training data. However, existing HSI few-shot classification methods often encounter the problem of insufficient domain-transferable knowledge learning that is either f…