Shot Hyperspectral Image Classification

Shot hyperspectral image classification focuses on accurately identifying materials in images containing many spectral bands, even with limited labeled data (few-shot learning). Current research emphasizes improving classification accuracy by leveraging advanced architectures like prototype networks and transformers, which learn spatial-spectral relationships and handle the challenges posed by boundary pixels and cross-domain variations. These advancements are crucial for applications requiring efficient analysis of hyperspectral data, such as remote sensing and medical imaging, where obtaining large labeled datasets can be difficult or expensive. The development of robust few-shot methods is therefore vital for expanding the practical utility of hyperspectral imaging.