DMAE-HU: A novel deep multitasking autoencoder for hybrid hyperspectral unmixing in remote sensing
ICT ExpressHyperspectral unmixing (HU) is crucial for extracting material information from hyperspectral images (HSI) obtained through remote sensing. Although linear unmixing methods are widely used due to their simplicity, they only address linear mixing effects.
Suresh Aala +8 more
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Efficient Blind Hyperspectral Unmixing Framework Based on CUR Decomposition (CUR-HU)
Remote SensingHyperspectral imaging captures detailed spectral data for remote sensing. However, due to the limited spatial resolution of hyperspectral sensors, each pixel of a hyperspectral image (HSI) may contain information from multiple materials.
Muhammad A. A. Abdelgawad +2 more
doaj +4 more sources
Maximum Likelihood Estimation Based Nonnegative Matrix Factorization for Hyperspectral Unmixing
Remote Sensing, 2021 Hyperspectral unmixing (HU) is a research hotspot of hyperspectral remote sensing technology. As a classical HU method, the nonnegative matrix factorization (NMF) unmixing method can decompose an observed hyperspectral data matrix into the product of two
Qin Jiang +4 more
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Non-Negative Matrix Factorization Based on Smoothing and Sparse Constraints for Hyperspectral Unmixing [PDF]
Sensors, 2022 Hyperspectral unmixing (HU) is a technique for estimating a set of pure source signals (end members) and their proportions (abundances) from each pixel of the hyperspectral image.
Xiangxiang Jia, Baofeng Guo
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Multi-stage convolutional autoencoder network for hyperspectral unmixing
International Journal of Applied Earth Observation and Geoinformation, 2022 Hyperspectral unmixing (HU) is a fundamental and critical task in various hyperspectral image (HSI) applications. Over the past few years, the linear mixing model (LMM) has received widely attention for its high efficiency, definite physical meaning, and
Xiaoguang Mei, Fan Fan
exaly +3 more sources
A Multiscale Hierarchical Model for Sparse Hyperspectral Unmixing
Remote Sensing, 2019 Due to the complex background and low spatial resolution of the hyperspectral sensor, observed ground reflectance is often mixed at the pixel level.
Jinlin Zou, Jinhui Lan
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A Modified Huber Nonnegative Matrix Factorization Algorithm for Hyperspectral Unmixing
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Hypersepctral unmixing (HU) has been one of the most challenging tasks in hyperspectral image research. Recently, nonnegative matrix factorization (NMF) has shown its superiority in hyperspectral unmixing due to its flexible modeling and little prior ...
Ziyang Guo +4 more
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Hyperspectral Unmixing Using Robust Deep Nonnegative Matrix Factorization
Remote Sensing, 2023 Nonnegative matrix factorization (NMF) and its numerous variants have been extensively studied and used in hyperspectral unmixing (HU). With the aid of the designed deep structure, deep NMF-based methods demonstrate advantages in exploring the ...
Risheng Huang +4 more
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Endmember-Free Hyperspectral Unmixing
IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingUnmixing networks for hyperspectral images (HSIs) often need to be redesigned for each sensor and initialized with endmember-estimation algorithms, which limits cross-scene generalization.
Baisen Liu +6 more
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Spatial-Channel Multiscale Transformer Network for Hyperspectral Unmixing [PDF]
SensorsIn recent years, deep learning (DL) has been demonstrated remarkable capabilities in hyperspectral unmixing (HU) due to its powerful feature representation ability. Convolutional neural networks (CNNs) are effective in capturing local spatial information,
Haixin Sun +4 more
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