Advances in hyperspectral image and signal processing: A comprehensive overview of the state of the art
Recent advances in airborne and spaceborne hyperspectral imaging technology have
provided end users with rich spectral, spatial, and temporal information. They have made a …
provided end users with rich spectral, spatial, and temporal information. They have made a …
Hyperspectral unmixing based on nonnegative matrix factorization: A comprehensive review
Hyperspectral unmixing has been an important technique that estimates a set of
endmembers and their corresponding abundances from a hyperspectral image (HSI) …
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 …
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
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 …
each (possibly mixed) pixel as a combination of pure spectral signatures (endmembers) and …
Endnet: Sparse autoencoder network for endmember extraction and hyperspectral unmixing
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 …
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 …
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 …
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 …
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
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 …
mixed spectral data pertaining to various materials observed aiming at analyzing the …
Hyperspectral unmixing using sparsity-constrained deep nonnegative matrix factorization with total variation
Hyperspectral unmixing is an important processing step for many hyperspectral applications,
mainly including: 1) estimation of pure spectral signatures (endmembers) and 2) estimation …
mainly including: 1) estimation of pure spectral signatures (endmembers) and 2) estimation …