Results 11 to 20 of about 1,956 (163)

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
doaj   +2 more sources

Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches [PDF]

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2012
Imaging spectrometers measure electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than multispectral cameras.
Antonio Plaza, Jocelyn Chanussot, José M. Bioucas-dias, Mario Parente, Nicolas Dobigeon, Paul Gader, Qian Du, Senior Member, Senior Member   +8 more
core   +10 more sources

DMAE-HU: A novel deep multitasking autoencoder for hybrid hyperspectral unmixing in remote sensing

ICT Express
Hyperspectral 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, Prudhvi Krishna Pavuluri, Anuj Deshpande, Sravan Kumar Sikhakolli, Karthikeyan Elumalai, Sunil Chinnadurai, Eswar Panchakarla, Md. Abdul Latif Sarker, Dong Seog Han   +8 more
doaj   +2 more sources

Spatial-Channel Multiscale Transformer Network for Hyperspectral Unmixing [PDF]

Sensors
In 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, Qiuguang Cao, Fanlei Meng, Jingwen Xu, Mengdi Cheng   +4 more
doaj   +2 more sources

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

Remote Sensing
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 (HSI) may contain information from multiple materials.
Muhammad A. A. Abdelgawad, Ray C. C. Cheung, Hong Yan   +2 more
doaj   +2 more sources

Spectral tracing of deuterium for imaging glucose metabolism. [PDF]

Nat Biomed Eng, 2019
Cells and tissues often display pronounced spatial and dynamical metabolic heterogeneity. Common glucose-imaging techniques report glucose uptake or catabolism activity, yet do not trace the functional utilization of glucose-derived anabolic products ...
Zhang L, Shi L, Shen Y, Miao Y, Wei M, Qian N, Liu Y, Min W.   +7 more
europepmc   +3 more sources

Hyperspectral Image Classification: Potentials, Challenges, and Future Directions. [PDF]

Comput Intell Neurosci, 2022
Recent imaging science and technology discoveries have considered hyperspectral imagery and remote sensing. The current intelligent technologies, such as support vector machines, sparse representations, active learning, extreme learning machines, transfer learning, and deep learning, are typically based on the learning of the machines. These techniques
Datta D, Mallick PK, Bhoi AK, Ijaz MF, Shafi J, Choi J.   +5 more
europepmc   +2 more sources

Spatial Immunometabolism: Integrating Technologies to Decode Cellular Metabolism in Tissues. [PDF]

Eur J Immunol
This review highlights recent advances that enable spatially resolved analysis of immunometabolism within tissue microenvironments. Integrating mass spectrometry imaging, vibrational microscopy, and spatial omics reveals how metabolic organization shapes immune function in cancer and other pathologies.
Hartmann FJ.
europepmc   +2 more sources

Spectrometer-Less Remote Sensing Image Classification Based on Gate-Tunable van der Waals Heterostructures. [PDF]

Adv Sci (Weinh)
Artificial designed gate‐tunable wide‐spectral 2D‐vdWH GaTe0.5Se0.5/WSe2‐based photodetector, requiring no additional auxiliary components, can achieve an average UV‐Vis‐NIR remote sensing image classification accuracy of 87.00% on 6 prevalent hyperspectral datasets, which is competitive with the accuracy of 250–1000 nm hyperspectral data (88.72%).
Yu Y, Zhong M, Xiong T, Yang J, Hu P, Long H, Zhou Z, Xin K, Liu YY, Yang J, Qiao J, Liu D, Wei Z.   +12 more
europepmc   +2 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, Yifei Dong, Jiangtao Peng, Mei Yan, Yi Sun   +4 more
doaj   +1 more source