Results 11 to 20 of about 151 (115)

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

Hyperspectral image non-linear unmixing using joint extrinsic and intrinsic priors with L1/2-norms to non-negative matrix factorisation

Journal of Spectral Imaging, 2022
Hyperspectral unmixing (HU) is one of the most active emerging areas in image processing that estimates the hyperspectral image’s endmember and abundance.
K. Priya, K. K. Rajkumar
doaj   +1 more source

An Efficient Attention-Based Convolutional Neural Network That Reduces the Effects of Spectral Variability for Hyperspectral Unmixing

Applied Sciences, 2022
The purpose of hyperspectral unmixing (HU) is to obtain the spectral features of materials (endmembers) and their proportion (abundance) in a hyperspectral image (HSI).
Baohua Jin, Yunfei Zhu, Wei Huang, Qiqiang Chen, Sijia Li   +4 more
doaj   +1 more source

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, Anyou Min, Bing Yang, Junhong Chen, Hong Li   +4 more
doaj   +1 more source

Optimal segmentation and improved abundance estimation for superpixel-based Hyperspectral Unmixing

European Journal of Remote Sensing, 2022
Superpixel-based hyperspectral unmixing (HU) can effectively reduce spectral variability’s influence on unmixing performance. In the superpixel-based HU method, this study proposes a segmentation scale determination method to improve the accuracy of ...
Qiang Guan, Tongyu Xu, Shuai Feng, Fenghua Yu, Kai Song   +4 more
doaj   +1 more source

Trichodesmium Around Australia: A View From Space

Geophysical Research Letters, Volume 50, Issue 16, 28 August 2023., 2023
Abstract The cyanobacterium Trichodesmium is responsible for approximately half of the ocean's nitrogen input through nitrogen fixation. Although it was first recorded near Australia in the 18th century, the knowledge of where and when large quantity of Trichodesmium around Australia could be found is still lacking.
Lin Qi, Menghua Wang, Chuanmin Hu, Douglas G. Capone, Ajit Subramaniam, Edward J. Carpenter, Yuyuan Xie   +6 more
wiley   +1 more source

High‐resolution hyperspectral imagery from pushbroom scanners on unmanned aerial systems

Geoscience Data Journal, Volume 9, Issue 2, Page 221-234, November 2022., 2022
Hyperspectral remote sensing has been developed to detect individual absorption features related to specific chemical bonds in soils, liquids or gases; however, because UAV‐based pushbroom hyperspectral sensor technologies are relatively new, no public datasets are currently available.
Jae‐In Kim, Junhwa Chi, Ali Masjedi, John Evan Flatt, Melba M. Crawford, Ayman F. Habib, Joohan Lee, Hyun‐Cheol Kim   +7 more
wiley   +1 more source

Robust linear unmixing with enhanced constraint of classification for hyperspectral remote sensing imagery

IET Image Processing, Volume 16, Issue 13, Page 3557-3566, 13 November 2022., 2022
Abstract Although hyperspectral data, especially spaceborne images, are rich in spectral information, their spatial resolution is usually low due to the limitation of sensor design and other factors. Therefore, for the application of hyperspectral images, unmixing technology is a key processing technology, such as linear mixing model and its derived ...
Haoyang Yu, Jinxue Chi, Xiaodi Shang, Xueji Shen, Jocelyn Chanussot, Yimin Shi   +5 more
wiley   +1 more source