Results 1 to 10 of about 828,208 (170)

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

Integration of Raman Spectroscopy and Metabolomics for Early Breast Cancer Detection and Classification. [PDF]

Cancer Med
ABSTRACT Breast cancer, now the fourth leading cause of cancer‐related mortality worldwide, necessitates early detection for improved clinical outcomes. Conventional histopathology, though widely used, is invasive and subjective, limiting its utility in early‐stage diagnosis.
Li X, Ren H, Deng Y, Li Y, Hu F.
europepmc   +2 more sources

Satellite Remote Sensing of Alpine Vegetation Dynamics: Challenges and Perspectives. [PDF]

Glob Chang Biol
Satellite greening has become a key tool for monitoring alpine vegetation change, but a positive vegetation‐index trend is not an ecological observation in itself. This perspective shows that interpreting alpine greening requires addressing two sequential challenges: methodological complexity, which can bias trends during image processing, and ...
Bayle A.
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   +2 more sources

Deep Learning Integration in Optical Microscopy: Advancements and Applications. [PDF]

Microsc Res Tech
It explores the integration of DL into optical microscopy, focusing on key applications including image classification, segmentation, and computational reconstruction. ABSTRACT Optical microscopy is a cornerstone imaging technique in biomedical research, enabling visualization of subcellular structures beyond the resolution limit of the human eye ...
Lahari PV, Dutta S, Deeksha H, Patel SA, Dehury B, Mazumder N.   +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

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

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, D. Fernando, D. Jayasundara, D. Wickramasinghe, D.Y.L. Ranasinghe, G.M.R.I. Godaliyadda, M.P.B. Ekanayake, H.M.V.R. Herath, L. Ramanayake, N. Senarath, H.M.H.K. Weerasooriya   +10 more
doaj   +1 more source

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, Linlin Wang, Wen Zeng, Qingfeng Guan, Zhen Hu   +4 more
doaj   +1 more source