Hyperspectral image (HSI) has received considerable attention in the field of target detection
due to its powerful ability to capture the spectral information of land covers, and plenty of …

Anomaly detection has become an important remote sensing application due to the
abundant spectral and spatial information contained in hyperspectral images. Recently …

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 …

Hyperspectral anomaly detection (HAD) aims to identify anomalous objects that deviate from
surrounding backgrounds in an unlabeled hyperspectral image (HSI). Most available neural …

The detection of camouflaged targets in the complex background is a hot topic of current
research. The existing hyperspectral target detection algorithms do not take advantage of …

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 …

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

Autoencoders (AEs) are central to hyperspectral anomaly detection, given their impressive
efficacy. However, the current methodologies often neglect the global pixel similarity of the …

With the development of hyperspectral sensing technology, hyperspectral target detection
technology plays an important role in remote target detection. However, existing …

Due to the limitation of target size and spatial resolution, targets of interest in hyperspectral
images (HSIs) often appear as subpixel targets, which makes hyperspectral target detection …