Results 91 to 100 of about 930,547 (198)

Spatial and temporal scales in plant phenotyping for crop water stress assessment: A review

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Water stress is a major limiting factor for crop productivity worldwide, and its impacts are intensifying due to climate variability and increasing water scarcity. This review focuses on the spatial and temporal scales in plant phenotyping as a critical approach to improving crop water‐stress assessment and supporting precision water ...
Daniel Kingsley Cudjoe, Yazhou Sun, Sanai Li, Wenxuan Guo   +3 more
wiley   +1 more source

Linear Spectral Unmixing Algorithms for Abundance Fraction Estimation in Spectroscopy [PDF]

, 2023
Fluorescence spectroscopy is commonly used in modern biological and chemical studies, especially for cellular and molecular analysis. Since the measured fluorescence spectrum is the sum of the spectrum of each fluorophore in a sample, a reliable ...
Oh, Changin
core  

Microscopy insights into reef coral fluorescent proteins: Diversity, light energy transfer and biological function

Journal of Microscopy, Volume 304, Issue 3, Page 301-344, December 2026.
Abstract Fluorescent proteins (FPs) and chromoproteins (CPs) of reef‐building corals, which generate much of their vivid colouration, occupy a distinctive position at the interface of natural photonics, symbiosis biology and fluorescence microscopy.
Anya Salih
wiley   +1 more source

Spectral unmixing of LANDSAT thematic mapper data

, 1995
The observed spectral signature of pixels in remote sensing imagery in most cases is the result of the reflecting properties of a number of surface materials constituting the area of a pixel.
van der Meer, F.D.
core   +1 more source

SSF-Net: A Spatial–Spectral Features Integrated Autoencoder Network for Hyperspectral Unmixing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
In recent years, deep learning has received tremendous attention in the field of hyperspectral unmixing (HU) due to its powerful learning capabilities.
Bin Wang, Huizheng Yao, Dongmei Song, Jie Zhang, Han Gao   +4 more
doaj   +1 more source

Symmetric Off‐Axis Spectral Encoding Enables High‐Throughput Six‐Color Optical Sectioning Imaging

Nanophotonics, Volume 15, Issue 21, 12 November 2026.
Within an off‐axis imaging framework, symmetric spatial encoding enables differential separation of fluorescence components excited at different wavelengths while suppressing common‐mode out‐of‐focus background. Combined with parallel emission‐band detection and residual crosstalk correction, symmetric off‐axis spectral encoding microscopy (sym‐OSEM ...
Jiangjiang Zhao, Zhangheng Ding, Zhan Zhang, Jie Zhang, Shuhong Qi, Jing Yuan   +5 more
wiley   +1 more source

Hybrid Hyperspectral Unmixing Using Fusion Mamba and Performer Attention

IEEE Access
Hyperspectral unmixing is emerging as a cutting-edge technology with applications across various areas of remote sensing. Several deep learning models, including transformer-based models, have been developed to improve unmixing accuracy.
M. Sreejam, Agilandeeswari Loganathan
doaj   +1 more source

An Orthotopic Xenograft Model for Preclinical Evaluation of CAR T Cell Therapy in Glioblastoma

Current Protocols, Volume 6, Issue 11, November 2026.
Abstract Glioblastoma (GBM) is the most lethal primary brain tumor in adults, and despite intensive research efforts, effective therapeutic breakthroughs remain elusive. Consequently, the standard of care has remained largely unchanged for nearly 30 years, underscoring the need for robust preclinical models to evaluate experimental therapies.
Julian Hübner, Rhonda McFleder, Chi Wang Ip, Miriam Alb, Hermann Einsele, Michael Hudecek, Thomas Nerreter   +6 more
wiley   +1 more source

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   +1 more source

Kernel-Based Nonlinear Spectral Unmixing with Dictionary Pruning

Remote Sensing, 2019
Spectral unmixing extracts subpixel information by decomposing observed pixel spectra into a collection of constituent spectra signatures and their associated fractions.
Zeng Li, Jie Chen, Susanto Rahardja
doaj   +1 more source