Spatial validation of spectral unmixing results: A systematic review

RM Cavalli - Remote Sensing, 2023 - mdpi.com
The pixels of remote images often contain more than one distinct material (mixed pixels),
and so their spectra are characterized by a mixture of spectral signals. Since 1971, a shared …

Sparse and low-rank constrained tensor factorization for hyperspectral image unmixing

P Zheng, H Su, Q Du - IEEE Journal of Selected Topics in …, 2021 - ieeexplore.ieee.org
Third-order tensors have been widely used in hyperspectral remote sensing because of their
ability to maintain the 3-D structure of hyperspectral images. In recent years, hyperspectral …

Convolutional autoencoder for blind hyperspectral image unmixing

Y Ranasinghe, S Herath… - 2020 IEEE 15th …, 2020 - ieeexplore.ieee.org
In the remote sensing context spectral unmixing is a technique to decompose a mixed pixel
into two fundamental representatives: endmembers and abundances. In this paper, a novel …

Nonnegative matrix factorization with entropy regularization for hyperspectral unmixing

J Liu, S Yuan, X Zhu, Y Huang… - International Journal of …, 2021 - Taylor & Francis
Nonnegative matrix factorization (NMF) has been one of the most widely used techniques for
hyperspectral unmixing (HU), which aims at decomposing each mixed pixel into a set of …

[HTML][HTML] Multiscale NMF based on intra-pixel and inter-pixel structure adjustment for spectral unmixing

T Yang, M Song, S Li, H Bao - … Journal of Applied Earth Observation and …, 2024 - Elsevier
Various improved nonnegative matrix factorization (NMF) methods have been widely used
in spectral unmixing (SU), including nonlinear versions to counter for the lower spatial …

Deep deterministic independent component analysis for hyperspectral unmixing

H Li, S Yu, JC Príncipe - ICASSP 2022-2022 IEEE International …, 2022 - ieeexplore.ieee.org
We develop a new neural network based independent component analysis (ICA) method by
directly minimizing the dependence amongst all extracted components. Using the matrix …

Mutual Incoherence and Relative Total Variation Regularizations for Blind Hyperspectral Unmixing

FX Song, C Kan, SW Deng - IEEE Transactions on Geoscience …, 2024 - ieeexplore.ieee.org
The unsupervised hyperspectral unmixing (HU) technique, also known as blind HU, directly
decomposes the mixed pixels of a hyperspectral image (HSI) into a combination of …

Gauss: Guided encoder-decoder architecture for hyperspectral unmixing with spatial smoothness

Y Ranasinghe, K Weerasooriya, R Godaliyadda… - arXiv preprint arXiv …, 2022 - arxiv.org
In recent hyperspectral unmixing (HU) literature, the application of deep learning (DL) has
become more prominent, especially with the autoencoder (AE) architecture. We propose a …

Low-rank and sparse NMF based on compression and correlation sensing for hyperspectral unmixing

T Yang, S Li, M Song, C Yu, H Bao - Infrared Physics & Technology, 2024 - Elsevier
Nonnegative matrix factorization (NMF) can obtain endmembers and abundances
simultaneously, and has attracted a lot of interest in hyperspectral unmixing. However, it is …

Efficient Blind Hyperspectral Unmixing Framework Based on CUR Decomposition (CUR-HU)

MAA Abdelgawad, RCC Cheung, H Yan - Remote Sensing, 2024 - mdpi.com
Hyperspectral imaging captures detailed spectral data for remote sensing. However, due to
the limited spatial resolution of hyperspectral sensors, each pixel of a hyperspectral image …