Hyperspectral Target Detection

Hyperspectral target detection (HTD) aims to identify specific objects within complex hyperspectral imagery by analyzing their unique spectral signatures. Recent research emphasizes improving HTD accuracy and efficiency through advanced deep learning architectures, such as transformers and novel spatial-spectral fusion networks, often incorporating techniques like contrastive learning and attention mechanisms to better handle spectral variations and limited training data. These advancements are driving improvements in applications ranging from remote sensing and environmental monitoring to defense and security, where precise object identification from hyperspectral data is crucial. The development of larger, more diverse datasets is also a key focus to further enhance model robustness and generalizability.