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

Without any prior information, hyperspectral anomaly detection is devoted to locating targets
of interest within a specific scene by exploiting differences in spectral characteristics …

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 …

Linear-based low-rank and sparse models (LRSM) and nonlinear-based deep autoencoder
(DAE) models have been proven to be effective for the task of anomaly detection (AD) in …

Hyperspectral target detection (HTD) is an important issue in Earth observation, with
applications in both military and civilian domains. However, conventional representation …

Many existing low-rank methods have achieved good detection performance in uniform
scenes, but they suffer from a high false alarm rate in complex noisy scenes. Therefore, it is …

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 …

Spectral super resolution (SSR) aims to reconstruct the 3D hyperspectral signal from a 2D
RGB image, which is prosperous with the proliferation of Convolutional Neural Networks …

Detecting subpixel targets is a considerably challenging issue in hyperspectral image
processing and interpretation. Most of the existing hyperspectral subpixel target detection …

As an advanced technique in remote sensing, hyperspectral target detection (HTD) is widely
concerned in civilian and military applications. However, the limitation of prior and mixed …