A survey on data‐efficient algorithms in big data era
A Adadi - Journal of Big Data, 2021 - Springer
The leading approaches in Machine Learning are notoriously data-hungry. Unfortunately,
many application domains do not have access to big data because acquiring data involves a …
many application domains do not have access to big data because acquiring data involves a …
CyCU-Net: Cycle-consistency unmixing network by learning cascaded autoencoders
In recent years, deep learning (DL) has attracted increasing attention in hyperspectral
unmixing (HU) applications due to its powerful learning and data fitting ability. The …
unmixing (HU) applications due to its powerful learning and data fitting ability. The …
Using low-rank representation of abundance maps and nonnegative tensor factorization for hyperspectral nonlinear unmixing
Tensor-based methods have been widely studied to attack inverse problems in
hyperspectral imaging since a hyperspectral image (HSI) cube can be naturally represented …
hyperspectral imaging since a hyperspectral image (HSI) cube can be naturally represented …
Deep autoencoders with multitask learning for bilinear hyperspectral unmixing
Hyperspectral unmixing is an important problem for remotely sensed data interpretation. It
amounts at estimating the spectral signatures of the pure spectral constituents in the scene …
amounts at estimating the spectral signatures of the pure spectral constituents in the scene …
UnDAT: Double-aware transformer for hyperspectral unmixing
Deep-learning-based methods have attracted increasing attention on hyperspectral
unmixing, where the transformer models have shown promising performance. However …
unmixing, where the transformer models have shown promising performance. However …
An improved hyperspectral unmixing approach based on a spatial–spectral adaptive nonlinear unmixing network
X Chen, X Zhang, M Ren, B Zhou… - IEEE Journal of …, 2023 - ieeexplore.ieee.org
The autoencoder (AE) framework is usually adopted as a baseline network for hyperspectral
unmixing. Totally an AE performs well in hyperspectral unmixing through automatically …
unmixing. Totally an AE performs well in hyperspectral unmixing through automatically …
An NMF-based method for jointly handling mixture nonlinearity and intraclass variability in hyperspectral blind source separation
Y Deville, G Faury, V Achard, X Briottet - Digital Signal Processing, 2023 - Elsevier
Considering a set of observed signals that result from mixing (ie combining) a set of
unknown source signals by means of an unknown function, blind source separation (BSS) …
unknown source signals by means of an unknown function, blind source separation (BSS) …
[图书][B] Nonlinear Blind Source Separation and Blind Mixture Identification: Methods for Bilinear, Linear-quadratic and Polynomial Mixtures
This book provides a detailed survey of the methods that were recently developed to handle
advanced versions of the blind source separation problem, which involve several types of …
advanced versions of the blind source separation problem, which involve several types of …
Modeling and Unsupervised Unmixing Based on Spectral Variability for Hyperspectral Oceanic Remote Sensing Data with Adjacency Effects
In a previous paper, we introduced (i) a specific hyperspectral mixing model for the sea
bottom, based on a detailed physical analysis that includes the adjacency effect, and (ii) an …
bottom, based on a detailed physical analysis that includes the adjacency effect, and (ii) an …
A staged approach with structural sparsity for hyperspectral unmixing
Accurate identification of pure substance and mapping the corresponding distribution are
challenging because of the existence of complex distribution mixed pixels in hyperspectral …
challenging because of the existence of complex distribution mixed pixels in hyperspectral …