Recently, embedding and metric-based few-shot learning (FSL) has been introduced into
hyperspectral image classification (HSIC) and achieved impressive progress. To further …

Target detection is the field of hyperspectral imaging where the materials or objects of
interest are detected from images captured by hyperspectral sensors. This methodology has …

Hyperspectral imagery with very high spectral resolution provides a new insight for subtle
nuances identification of similar substances. However, hyperspectral target detection faces …

Deep learning (DL) has shown great potential for hyperspectral image (HSI) classification
due to its powerful ability of nonlinear modeling and end-to-end optimization. However, DL …

When predicting data for which limited supervised information is available, hyperspectral
target detection methods based on deep transfer learning expect that the network will not …

Deep learning-based hyperspectral target detection (HTD) methods are limited by the lack of
prior information. Self-supervised learning is a kind of unsupervised learning, which mainly …

Deep learning (DL) has made significant progress in hyperspectral target detection (HTD) in
recent years. However, the existing DL-based HTD methods generally generate numerous …

3 天津(滨海) 人工智能创新中心天津300457 摘要数据驱动下的深度学**技术在计算机视觉领域
取得重大突破, 但模型的高性能严重依赖于大量标注样本的训练. 然而在实际场景当中 …

Target detection in hyperspectral images (HSIs) mainly relies on the spectral information of
the target prior. However, prior spectra with precise variation information are often hard to …

Hyperspectral images (HSIs) possess the capability to accurately characterize the attribute
information of objects. However, they are usually obtained at a high spectral resolution with …