Results 81 to 90 of about 1,511 (182)

Transformer for Multitemporal Hyperspectral Image Unmixing

IEEE Transactions on Image Processing
Multitemporal hyperspectral image unmixing (MTHU) holds significant importance in monitoring and analyzing the dynamic changes of surface. However, compared to single-temporal unmixing, the multitemporal approach demands comprehensive consideration of information across different phases, rendering it a greater challenge.
Hang Li, Qiankun Dong, Xueshuo Xie, Xia Xu, Tao Li 0022, Zhenwei Shi 0001   +5 more
openaire   +3 more sources

Spectral-Spatial Hyperspectral Unmixing Using Multitask Learning

IEEE Access, 2019
Hyperspectral unmixing is an important and challenging task in the field of remote sensing which arises when the spatial resolution of sensors is insufficient for the separation of spectrally distinct materials.
Burkni Palsson, Johannes R. Sveinsson, Magnus O. Ulfarsson   +2 more
doaj   +1 more source

Updated Homogeneity Criteria Based Low-Dimensional Representation for Hyperspectral Unmixing

IEEE Access
Superpixel-based approaches have been proposed for hyperspectral unmixing. The basic assumption of this approach is that the superpixel over-segmentation segments the image into small homogeneous areas.
Jiarui Yi, Huiyi Gao
doaj   +1 more source

A Global-to-Local Spectral-Spatial Attention-Based Nonlinearity and Scaled Endmember Variability Parametric Learning Network for Unmixing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Hyperspectral unmixing has attracted increasing attention in remote sensing applications. Unfortunately, significant unmixing residuals often arise from the coupled nonlinear mixing effects and spectral variability (SV), bringing challenges for reliably ...
Yi Zhao, Bin Yang
doaj   +1 more source

Preprocessing Algorithm Leveraging Geometric Modeling for Scale Correction in Hyperspectral Images for Improved Unmixing Performance

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Spectral variability significantly impacts the accuracy and convergence of hyperspectral unmixing algorithms. Many methods address complex spectral variability; yet large-scale distortions to the scale of the observed pixel signatures due to topography ...
Praveen Sumanasekara, Athulya Ratnayake, Buddhi Wijenayake, Keshawa Ratnayake, Roshan Godaliyadda, Parakrama Ekanayake, Vijitha Herath   +6 more
doaj   +1 more source

A Deep Equilibrium Network for Hyperspectral Unmixing

CoRR
Hyperspectral unmixing (HU) is crucial for analyzing hyperspectral imagery, yet achieving accurate unmixing remains challenging. While traditional methods struggle to effectively model complex spectral-spatial features, deep learning approaches often lack physical interpretability.
Chentong Wang, Jincheng Gao, Fei Zhu, Jie Chen   +3 more
openaire   +2 more sources

Joint Hyperspectral Image Deconvolution and Unmixing via Plug-and-Play Priors

Remote Sensing
Hyperspectral imaging (HSI) provides rich spatial and spectral information for remote sensing, mineral exploration, and biomedical analysis, but its limited spatial resolution and sensor imperfections lead to blurred, noisy, and mixed-pixel observations.
Sina Layazali, Chrysanthe Preza
doaj   +1 more source

An Open Evaluation of Hyperspectral Unmixing Strategies for EDS Analysis. [PDF]

Microsc Microanal, 2018
Taillon JA.
europepmc   +1 more source

Contributions to Hyperspectral Unmixing

Contribution au démélange hyperspectral Le démelangeage spectral est un domaine de recherche actif qui trouve des applications dans des domaines variés comme la télédétection, le traitement des signaux audio ou la chimie. Dans le contexte des capteurs hyper spectraux, les images acquises sont souvent de faible résolution spatiale ...
openaire   +2 more sources

A Dye-Free Analog to Retinal Angiography Using Hyperspectral Unmixing to Retrieve Oxyhemoglobin Abundance. [PDF]

Transl Vis Sci Technol, 2019
Dwight JG, Weng CY, Pawlowski ME, Tkaczyk TS.   +3 more
europepmc   +1 more source