Hyperspectral Image
Hyperspectral imaging (HSI) captures detailed spectral information across numerous bands, enabling precise material identification and scene analysis beyond the capabilities of traditional RGB or multispectral imaging. Current research heavily focuses on improving HSI classification and reconstruction using advanced deep learning architectures, such as transformers and state-space models (SSMs), often incorporating spatial context and addressing challenges like computational efficiency and data scarcity through techniques like self-supervised learning and test-time training. These advancements have significant implications for diverse fields, including remote sensing, precision agriculture, medical imaging, and environmental monitoring, offering enhanced capabilities for material identification, object detection, and scene understanding.
Papers
Hyperspectral Neural Radiance Fields
Gerry Chen, Sunil Kumar Narayanan, Thomas Gautier Ottou, Benjamin Missaoui, Harsh Muriki, Cédric Pradalier, Yongsheng Chen
Varroa destructor detection on honey bees using hyperspectral imagery
Zina-Sabrina Duma, Tomas Zemcik, Simon Bilik, Tuomas Sihvonen, Peter Honec, Satu-Pia Reinikainen, Karel Horak
Hyperspectral Image Analysis in Single-Modal and Multimodal setting using Deep Learning Techniques
Shivam Pande
Multiview Subspace Clustering of Hyperspectral Images based on Graph Convolutional Networks
Xianju Li, Renxiang Guan, Zihao Li, Hao Liu, Jing Yang
LUM-ViT: Learnable Under-sampling Mask Vision Transformer for Bandwidth Limited Optical Signal Acquisition
Lingfeng Liu, Dong Ni, Hangjie Yuan