Results 21 to 30 of about 919,951 (170)

Mamba-based spatial-spectral fusion network for hyperspectral unmixing

Journal of King Saud University: Computer and Information Sciences
Hyperspectral unmixing (HU) is a critical technique in hyperspectral image (HSI) analysis, aimed at decomposing mixed pixels into a set of spectral signatures (endmembers) and their corresponding abundance values.
Yuquan Gan, Jingtao Wei, Mengmeng Xu
doaj   +2 more sources

SCSU–GDO: Superpixel Collaborative Sparse Unmixing with Graph Differential Operator for Hyperspectral Imagery

Remote Sensing
In recent years, remarkable advancements have been achieved in hyperspectral unmixing (HU). Sparse unmixing, in which models mix pixels as linear combinations of endmembers and their corresponding fractional abundances, has become a dominant paradigm in ...
Kaijun Yang, Zhixin Zhao, Qishen Yang, Ruyi Feng   +3 more
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

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

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

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

Synthesis of Synthetic Hyperspectral Images with Controllable Spectral Variability Using a Generative Adversarial Network

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, Magnus O. Ulfarsson, Johannes R. Sveinsson   +2 more
doaj   +1 more source

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, Johannes R. Sveinsson, Magnus O. Ulfarsson   +2 more
doaj   +1 more source