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 …

Hyperspectral unmixing based on nonnegative matrix factorization: A comprehensive review

XR Feng, HC Li, R Wang, Q Du, X Jia… - IEEE Journal of …, 2022 - ieeexplore.ieee.org
Hyperspectral unmixing has been an important technique that estimates a set of
endmembers and their corresponding abundances from a hyperspectral image (HSI) …

[PDF][PDF] 中国高光谱遥感的前沿进展

童庆禧, 张兵, 张立福 - 遥感学报, 2016 - hrs-cas.com
高光谱成像技术具有光谱分辨率高, 图谱合一的独特优势, 是遥感技术发展以来最重大的科技
突破之一. 中国的高光谱遥感发展与国际基本同步, 在国家和省部级科研项目的支持下 …

DAEN: Deep autoencoder networks for hyperspectral unmixing

Y Su, J Li, A Plaza, A Marinoni, P Gamba… - … on Geoscience and …, 2019 - ieeexplore.ieee.org
Spectral unmixing is a technique for remotely sensed image interpretation that expresses
each (possibly mixed) pixel as a combination of pure spectral signatures (endmembers) and …

Endnet: Sparse autoencoder network for endmember extraction and hyperspectral unmixing

S Ozkan, B Kaya, GB Akar - IEEE Transactions on Geoscience …, 2018 - ieeexplore.ieee.org
Data acquired from multichannel sensors are a highly valuable asset to interpret the
environment for a variety of remote sensing applications. However, low spatial resolution is …

The why and how of nonnegative matrix factorization

N Gillis - … , optimization, kernels, and support vector machines, 2014 - books.google.com
Nonnegative matrix factorization (NMF) has become a widely used tool for the analysis of
high-dimensional data as it automatically extracts sparse and meaningful features from a set …

Hyperspectral unmixing with spectral variability using a perturbed linear mixing model

PA Thouvenin, N Dobigeon… - IEEE Transactions on …, 2015 - ieeexplore.ieee.org
Given a mixed hyperspectral data set, linear unmixing aims at estimating the reference
spectral signatures composing the data-referred to as endmembers-their abundance …

[图书][B] Nonnegative matrix factorization

N Gillis - 2020 - SIAM
Identifying the underlying structure of a data set and extracting meaningful information is a
key problem in data analysis. Simple and powerful methods to achieve this goal are linear …

Hyperspectral unmixing for additive nonlinear models with a 3-D-CNN autoencoder network

M Zhao, M Wang, J Chen… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Spectral unmixing is an important task in hyperspectral image processing for separating the
mixed spectral data pertaining to various materials observed aiming at analyzing the …

Hyperspectral unmixing using sparsity-constrained deep nonnegative matrix factorization with total variation

XR Feng, HC Li, J Li, Q Du, A Plaza… - IEEE Transactions on …, 2018 - ieeexplore.ieee.org
Hyperspectral unmixing is an important processing step for many hyperspectral applications,
mainly including: 1) estimation of pure spectral signatures (endmembers) and 2) estimation …