Results 51 to 60 of about 9,243 (189)

Deep Learning Integration in Optical Microscopy: Advancements and Applications

Microscopy Research and Technique, EarlyView.
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 ...
Pottumarthy Venkata Lahari, Sagnika Dutta, H. Deeksha, Samreen A. Patel, Budheswar Dehury, Nirmal Mazumder   +5 more
wiley   +1 more source

Gradients in urban material composition: A new concept to map cities with spaceborne imaging spectroscopy data [PDF]

, 2019
To understand processes in urban environments, such as urban energy fluxes or surface temperature patterns, it is important to map urban surface materials. Airborne imaging spectroscopy data have been successfully used to identify urban surface materials
Feilhauer, Hannes, Heiden, Uta, Jilge, Marianne, Neumann, Carsten   +3 more
core   +2 more sources

Adaptive Graph Regularized Multilayer Nonnegative Matrix Factorization for Hyperspectral Unmixing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020
Hyperspectral unmixing is an important technique for remote sensing image analysis. Among various unmixing techniques, nonnegative matrix factorization (NMF) shows unique advantage in providing a unified solution with well physical interpretation.
Lei Tong, Jun Zhou, Bin Qian, Jing Yu, Chuangbai Xiao   +4 more
doaj   +1 more source

AI‐Enhanced Surface‐Enhanced Raman Scattering for Accurate and Sensitive Biomedical Sensing

Advanced Intelligent Discovery, Volume 2, Issue 1, February 2026.
AI‐SERS advances spectral interpretation with greater precision and speed, enhancing molecular detection, biomedical analysis, and imaging. This review explores its essential contributions to biofluid analysis, disease identification, therapeutic agent evaluation, and high‐resolution biomedical imaging, aiding diagnostic decision‐making.
Seungki Lee, Rowoon Park, Ho Sang Jung
wiley   +1 more source

Multilayer Structured NMF for Spectral Unmixing of Hyperspectral Images

, 2015
One of the challenges in hyperspectral data analysis is the presence of mixed pixels. Mixed pixels are the result of low spatial resolution of hyperspectral sensors. Spectral unmixing methods decompose a mixed pixel into a set of endmembers and abundance
Ghassemian, Hassan, Rajabi, Roozbeh
core   +1 more source

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

IET Image Processing, 2022
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.
Haoyang Yu, Jinxue Chi, Xiaodi Shang, Xueji Shen, Jocelyn Chanussot, Yimin Shi   +5 more
doaj   +1 more source

AVIRIS‐3 Rapid Response to January 2025 Los Angeles Wildfires

Geophysical Research Letters, Volume 53, Issue 2, 28 January 2026.
Abstract Wildfires in wildland‐urban interfaces (WUIs) are a growing concern due to their devastating impact on human communities and ecosystems. Low‐latency impact assessment is critical for wildfire response, yet immediate access to fire‐affected communities can be limited.
Megan Ward‐Baranyay, Red Willow Coleman, Mark Wronkiewicz, Francisco Ochoa, Daniel Sousa, Ceth W. Parker, David R. Thompson, Clayton D. Elder, Gregory S. Okin, K. Dana Chadwick, Kimberley Miner, John Chapman, Regina Eckert, Daniel Jensen, Robert O. Green, Philip G. Brodrick   +15 more
wiley   +1 more source

Robust Linear Spectral Unmixing using Anomaly Detection

, 2015
This paper presents a Bayesian algorithm for linear spectral unmixing of hyperspectral images that accounts for anomalies present in the data. The model proposed assumes that the pixel reflectances are linear mixtures of unknown endmembers, corrupted by ...
Altmann, Yoann, Hero, Alfred, McLaughlin, Steve   +2 more
core   +1 more source

Sparse Unmixing for Hyperspectral Imagery via Comprehensive-Learning-Based Particle Swarm Optimization

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021
Sparse unmixing methods have been extensively studied as a popular topic in hyperspectral image analysis for several years. Fundamental model-based unmixing problems can be better reformulated by exploiting sparse constraints in different forms. Gradient-
Yapeng Miao, Bin Yang
doaj   +1 more source

Combinatorial SiO2‐Encapsulated Quantum Dot Nanoparticles and their Use in Spectral Unmixing Analysis

Analysis &Sensing, Volume 6, Issue 1, January 2026.
Combinatorial QD‐SiO2 nanoparticles combined with linear unmixing of the photoluminescence spectrum increase the multiplexity of assays. Linear unmixing spectral analysis is a technique where signals from tens of fluorophores can be deconvoluted to increase multiplexing by 4–5‐fold.
Yuwei Wang, Jennifer I. L. Chen
wiley   +1 more source