Advances in hyperspectral image and signal processing: A comprehensive overview of the state of the art

P Ghamisi, N Yokoya, J Li, W Liao, S Liu… - … and Remote Sensing …, 2017 - ieeexplore.ieee.org
Recent advances in airborne and spaceborne hyperspectral imaging technology have
provided end users with rich spectral, spatial, and temporal information. They have made a …

Spectral variability in hyperspectral data unmixing: A comprehensive review

RA Borsoi, T Imbiriba, JCM Bermudez… - … and remote sensing …, 2021 - ieeexplore.ieee.org
The spectral signatures of the materials contained in hyperspectral images, also called
endmembers (EMs), can be significantly affected by variations in atmospheric, illumination …

Interpretable hyperspectral artificial intelligence: When nonconvex modeling meets hyperspectral remote sensing

D Hong, W He, N Yokoya, J Yao, L Gao… - … and Remote Sensing …, 2021 - ieeexplore.ieee.org
Hyperspectral (HS) imaging, also known as image spectrometry, is a landmark technique in
geoscience and remote sensing (RS). In the past decade, enormous efforts have been made …

Total-variation-regularized low-rank matrix factorization for hyperspectral image restoration

W He, H Zhang, L Zhang, H Shen - IEEE transactions on …, 2015 - ieeexplore.ieee.org
In this paper, we present a spatial spectral hyperspectral image (HSI) mixed-noise removal
method named total variation (TV)-regularized low-rank matrix factorization (LRTV). In …

Hyperspectral remote sensing data analysis and future challenges

JM Bioucas-Dias, A Plaza… - … and remote sensing …, 2013 - ieeexplore.ieee.org
Hyperspectral remote sensing technology has advanced significantly in the past two
decades. Current sensors onboard airborne and spaceborne platforms cover large areas of …

Convolutional autoencoder for spectral–spatial hyperspectral unmixing

B Palsson, MO Ulfarsson… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Blind hyperspectral unmixing is the process of expressing the measured spectrum of a pixel
as a combination of a set of spectral signatures called endmembers and simultaneously …

Graph-based semi-supervised learning: A review

Y Chong, Y Ding, Q Yan, S Pan - Neurocomputing, 2020 - Elsevier
Considering the labeled samples may be difficult to obtain because they require human
annotators, special devices, or expensive and slow experiments. Semi-supervised learning …

A review of nonlinear hyperspectral unmixing methods

R Heylen, M Parente, P Gader - IEEE Journal of Selected …, 2014 - ieeexplore.ieee.org
In hyperspectral unmixing, the prevalent model used is the linear mixing model, and a large
variety of techniques based on this model has been proposed to obtain endmembers and …

Hyperspectral unmixing overview: Geometrical, statistical, and sparse regression-based approaches

JM Bioucas-Dias, A Plaza, N Dobigeon… - IEEE journal of …, 2012 - ieeexplore.ieee.org
Imaging spectrometers measure electromagnetic energy scattered in their instantaneous
field view in hundreds or thousands of spectral channels with higher spectral resolution than …

Hyperspectral unmixing using transformer network

P Ghosh, SK Roy, B Koirala, B Rasti… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Transformers have intrigued the vision research community with their state-of-the-art
performance in natural language processing. With their superior performance, transformers …