The spectral signatures of the materials contained in hyperspectral images, also called
endmembers (EMs), can be significantly affected by variations in atmospheric, illumination …

Hyperspectral imagery can obtain hundreds of narrow spectral bands of ground objects. The
abundant and detailed spectral information offers a unique diagnostic identification ability for …

The Consultative Committee for Space Data Systems (CCSDS) published the CCSDS 123.0-
B-2,“Low-Complexity Lossless and Near-Lossless Multispectral and Hyperspectral Image …

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

Recently, deep learning methods have demonstrated their potentials in extracting spectral
information for hyperspectral images and have been widely applied in hyperspectral target …

Target detection plays a core issue in hyperspectral remote sensing, but faces serious
challenges of how to deal with the spatial and spectral redundancies and spectral variations …

Recurrent neural networks and Transformers have recently dominated most applications in
hyperspectral (HS) imaging, owing to their capability to capture long-range dependencies …

Spectral unmixing expresses the mixed pixels existing in hyperspectral images as the
product of endmembers and their corresponding fractional abundances, which has been …

Deep learning-based hyperspectral target detection (HTD) is potentially hindered by the
limited training samples and sensor-dependent transferability. To address this issue, we …

In hyperspectral target detection, the conventional metric learning-based algorithms provide
unique advantages in detecting targets as they do not require specific assumptions and …