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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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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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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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
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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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GAUSS: Guided encoder - decoder Architecture for hyperspectral Unmixing with Spatial Smoothness
European Journal of Remote Sensing, 2023 This study introduces GAUSS (Guided encoder-decoder Architecture for hyperspectral Unmixing with Spatial Smoothness), a novel autoencoder-based architecture for hyperspectral unmixing (HU).
H.M.K.D. Wickramathilaka +10 more
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A Sparse Topic Relaxion and Group Clustering Model for Hyperspectral Unmixing
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Hyperspectral unmixing (HU) has been a hot research topic in the field of hyperspectral remote sensing. In recent years, the employment of the probabilistic topic model to acquire the latent topics of hyperspectral images has been an effective method for
Qiqi Zhu +4 more
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Multi-stage convolutional autoencoder network for hyperspectral unmixing
International Journal of Applied Earth Observations 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
Yang Yu +5 more
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Remote Sensing, 2023 In hyperspectral unmixing (HU), spectral variability in hyperspectral images (HSIs) is a major challenge which has received a lot of attention over the last few years.
Burkni Palsson +2 more
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Blind Hyperspectral Unmixing Using Autoencoders: A Critical Comparison
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022 Deep learning (DL) has heavily impacted the data-intensive field of remote sensing. Autoencoders are a type of DL methods that have been found to be powerful for blind hyperspectral unmixing (HU). HU is the process of resolving the measured spectrum of a
Burkni Palsson +2 more
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