Results 71 to 80 of about 9,243 (189)

Hyperspectral Imaging: The Intelligent Eye to Uncover the Password of Plant Science

Modern Agriculture, Volume 3, Issue 2, December 2025.
Hyperspectral imaging (HSI) has emerged as a powerful non‐destructive technique for characterisation of the plant phenotype and physiological traits. The ongoing development of cost‐effective hardware, coupled with standardised acquisition protocols and open‐access spectral libraries, is accelerating its integration with multi‐omics approaches to ...
Jingyan Song, Haifeng Liang, Bingjie Lu, Jing Guo, Yuan Gao, Xiao Hu, Manlin Yang, Xiaofan Li, Zhenyu Wang, Yongqi Chen, Yinyin Zhang, Shen Su, Zhangyun Gao, Shijie Li, Ping Chen, Jing Wang, Wanneng Yang, Hui Feng   +17 more
wiley   +1 more source

Empowering Biomedical Research with Foundation Models in Computational Microscopy: A Systematic Review

Advanced Intelligent Systems, Volume 7, Issue 11, November 2025.
The integration of foundation models into computational microscopy revolutionizes biomedical research by enhancing imaging resolution, accelerating data analysis, and enabling real‐time biological interpretation. This systematic review critically examines recent advancements, highlights translational challenges, and discusses the transformative ...
Di Ding, Tianliang Yao, Genyi Feng, Xueqing Gao, Xusen Sun, Rong Luo   +5 more
wiley   +1 more source

Nonlinear unmixing of hyperspectral images using a semiparametric model and spatial regularization

, 2013
Incorporating spatial information into hyperspectral unmixing procedures has been shown to have positive effects, due to the inherent spatial-spectral duality in hyperspectral scenes.
Chen, Jie, Hero III, Alfred O., Richard, Cédric   +2 more
core   +1 more source

Joint Local Block Grouping with Noise-Adjusted Principal Component Analysis for Hyperspectral Remote-Sensing Imagery Sparse Unmixing

Remote Sensing, 2019
Spatial regularized sparse unmixing has been proved as an effective spectral unmixing technique, combining spatial information and standard spectral signatures known in advance into the traditional spectral unmixing model in the form of sparse regression.
Ruyi Feng, Lizhe Wang, Yanfei Zhong
doaj   +1 more source

Spatial Immunometabolism: Integrating Technologies to Decode Cellular Metabolism in Tissues

European Journal of Immunology, Volume 55, Issue 11, November 2025.
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.
Felix J. Hartmann
wiley   +1 more source

Conventional to Deep Learning Methods for Hyperspectral Unmixing: A Review

Remote Sensing
Hyperspectral images often contain many mixed pixels, primarily resulting from their inherent complexity and low spatial resolution. To enhance surface classification and improve sub-pixel target detection accuracy, hyperspectral unmixing technology has ...
Jinlin Zou, Hongwei Qu, Peng Zhang
doaj   +1 more source

Remote sensing of soil organic carbon in varied tillage‐crop systems

Journal of Environmental Quality, Volume 54, Issue 6, Page 1535-1547, November/December 2025.
Abstract The use of remote sensing (RS) to estimate soil organic carbon (SOC) in cropland has become increasingly important to producers, researchers, and policy makers to assess soil and plant health across spatially variable landscapes. Yet, RS estimation of cropland SOC is challenging, particularly when mixed crop residues and soils are present. Our
Amy L. Zoller, Girma Birru, Tulsi Kharel, Virginia L. Jin, Marty R. Schmer, Ariel Freidenreich, Brian Wardlow, Tim Kettler, Tekleab Gala   +8 more
wiley   +1 more source

Deep Spectral Convolution Network for Hyperspectral Unmixing [PDF]

2018 25th IEEE International Conference on Image Processing (ICIP), 2018
In this paper, we propose a novel hyperspectral unmixing technique based on deep spectral convolution networks (DSCN). Particularly, three important contributions are presented throughout this paper. First, fully-connected linear operation is replaced with spectral convolutions to extract local spectral characteristics from hyperspectral signatures ...
Ozkan, Savas, Akar, Gozde Bozdagi
openaire   +2 more sources

Spectral Unmixing with Multiple Dictionaries

, 2017
Spectral unmixing aims at recovering the spectral signatures of materials, called endmembers, mixed in a hyperspectral or multispectral image, along with their abundances.
Cohen, Jeremy E., Gillis, Nicolas
core   +2 more sources

Fast Unmixing of Noisy Hyperspectral Images Based on Vertex Component Analysis and Singular Spectrum Analysis Algorithms

Canadian Journal of Remote Sensing, 2020
Efficient denoising is of great significance to unmixing hyperspectral images. In the present study, a fast unmixing method for noisy hyperspectral images based on the combination of vertex component analysis and singular spectrum analysis is proposed ...
Dongmei Song, Ning Sun, Mingming Xu, Bin Wang, Ling Zhang   +4 more
doaj   +1 more source