Results 41 to 50 of about 1,511 (182)

Real‐Time Multicolor Fluorescence Microscopy via Cross‐Channel Acquisition and Deep‐Learning‐Based Inference

Advanced Intelligent Discovery, EarlyView.
Sequential multicolor fluorescence imaging in dynamic microsystems is constrained by acquisition speed and excitation dose. This study introduces a real‐time framework to reconstruct spectrally separated channels from reduced cross‐channel acquisitions (frames containing mixed spectral contributions).
Juan J. Huaroto, Yuxin Jin, Nicholas Roy, Sarthak Misra   +3 more
wiley   +1 more source

Overcoming the Nyquist Limit in Molecular Hyperspectral Imaging by Reinforcement Learning

Advanced Intelligent Discovery, EarlyView.
Explorative spectral acquisition guide automatically selects informative spectral bands to optimize downstream tasks, outperforming full‐spectrum acquisition. The selected hyperspectral data are used for tasks such as unmixing and segmentation. BandOptiNet encodes selection states and outputs optimal bands to guide spectral acquisition. Recent advances
Xiaobin Tang, Yongqing Zhang, Xinyu Deng, Hyeon Jeong Lee, Delong Zhang   +4 more
wiley   +1 more source

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

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

Mapping Scytonemin Pigment Content and Biocrust Functional Indicators Using High‐Resolution Multispectral Imagery

Remote Sensing in Ecology and Conservation, EarlyView.
We demonstrate that high‐resolution multispectral imagery combined with machine learning can accurately predict key biocrust functional indicators, particularly the UV‐protective pigment scytonemin, across dryland ecosystems. Using fine‐scale imagery acquired in the Chihuahuan Desert and Colorado Plateau, we identified spectral bands strongly ...
Raúl Román, Tong Qiu, Fernando T. Maestre, Elizabeth A. La Rue, Sergio Vargas Zesati, Anthony Schaefer, Ryan V. Trexler, Nicole Pietrasiak, Estelle Couradeau   +8 more
wiley   +1 more source

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

Deep Learning‐Assisted Coherent Raman Scattering Microscopy

Advanced Intelligent Discovery, Volume 2, Issue 4, August 2026.
The analytical capabilities of coherent Raman scattering microscopy are augmented through deep learning integration. This synergistic paradigm improves fundamental performance via denoising, deconvolution, and hyperspectral unmixing. Concurrently, it enhances downstream image analysis including subcellular localization, virtual staining, and clinical ...
Jianlin Liu, Rujia Liu, Xinyan Zhang, Xun Chen, Shuhua Yue   +4 more
wiley   +1 more source

Superpixel-Guided Matrix-Valued Kernel Functions for Multiscale Nonlinear Hyperspectral Unmixing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Hyperspectral unmixing is a critical challenge in the analysis of hyperspectral remote sensing data. Due to the complex interactions between incident light and materials, which are significantly influenced by the three-dimensional geometry of the scene ...
Xiu Zhao, Meiping Song
doaj   +1 more source

Rolling Guidance Based Scale-Aware Spatial Sparse Unmixing for Hyperspectral Remote Sensing Imagery

Remote Sensing, 2017
Spatial regularization based sparse unmixing has attracted much attention in the hyperspectral remote sensing image processing field, which combines spatial information consideration with a sparse unmixing model, and has achieved improved fractional ...
Ruyi Feng, Yanfei Zhong, Lizhe Wang, Wenjuan Lin   +3 more
doaj   +1 more source

Cluster-Wise Weighted NMF for Hyperspectral Images Unmixing with Imbalanced Data

Remote Sensing, 2021
Hyperspectral unmixing is an important technique for analyzing remote sensing images which aims to obtain a collection of endmembers and their corresponding abundances.
Xiaochen Lv, Wenhong Wang, Hongfu Liu
doaj   +1 more source