Results 71 to 80 of about 1,956 (163)

Bi-Objective Nonnegative Matrix Factorization: Linear Versus Kernel-Based Models

, 2015
Nonnegative matrix factorization (NMF) is a powerful class of feature extraction techniques that has been successfully applied in many fields, namely in signal and image processing.
Honeine, Paul, Zhu, Fei
core   +3 more sources

Nonlinearity detection in hyperspectral images using a polynomial post-nonlinear mixing model [PDF]

, 2011
International audienceThis paper studies a nonlinear mixing model for hyperspectral image unmixing and nonlinearity detection. The proposed model assumes that the pixel reflectances are nonlinear functions of pure spectral components contaminated by an ...
Altmann, Yoann, Dobigeon, Nicolas, Tourneret, Jean-Yves   +2 more
core   +6 more sources

Monitoring and Modeling the Soil‐Plant System Toward Understanding Soil Health

Reviews of Geophysics, Volume 63, Issue 1, March 2025.
Abstract The soil health assessment has evolved from focusing primarily on agricultural productivity to an integrated evaluation of soil biota and biotic processes that impact soil properties. Consequently, soil health assessment has shifted from a predominantly physicochemical approach to incorporating ecological, biological and molecular microbiology
Yijian Zeng, Anne Verhoef, Harry Vereecken, Eyal Ben‐Dor, Tom Veldkamp, Liz Shaw, Martine Van Der Ploeg, Yunfei Wang, Zhongbo Su   +8 more
wiley   +1 more source

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   +1 more source

Hyperspectral image restoration using noise gradient and dual priors under mixed noise conditions

CAAI Transactions on Intelligence Technology, Volume 10, Issue 1, Page 72-93, February 2025.
Abstract Images obtained from hyperspectral sensors provide information about the target area that extends beyond the visible portions of the electromagnetic spectrum. However, due to sensor limitations and imperfections during the image acquisition and transmission phases, noise is introduced into the acquired image, which can have a negative impact ...
Hazique Aetesam, Suman Kumar Maji, V. B. Surya Prasath   +2 more
wiley   +1 more source

When geoscience meets generative AI and large language models: Foundations, trends, and future challenges

Expert Systems, Volume 41, Issue 10, October 2024.
Abstract Generative Artificial Intelligence (GAI) represents an emerging field that promises the creation of synthetic data and outputs in different modalities. GAI has recently shown impressive results across a large spectrum of applications ranging from biology, medicine, education, legislation, computer science, and finance.
Abdenour Hadid, Tanujit Chakraborty, Daniel Busby   +2 more
wiley   +1 more source

RGB‐guided hyperspectral image super‐resolution with deep progressive learning

CAAI Transactions on Intelligence Technology, Volume 9, Issue 3, Page 679-694, June 2024.
Abstract Due to hardware limitations, existing hyperspectral (HS) camera often suffer from low spatial/temporal resolution. Recently, it has been prevalent to super‐resolve a low resolution (LR) HS image into a high resolution (HR) HS image with a HR RGB (or multispectral) image guidance.
Tao Zhang, Ying Fu, Liwei Huang, Siyuan Li, Shaodi You, Chenggang Yan   +5 more
wiley   +1 more source

Endmember Independence and Bilateral Filtering Regularizations for Blind Hyperspectral Unmixing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Hyperspectral unmixing (HU) aims to decompose the mixed pixels of a hyperspectral image into endmembers weighted by their corresponding abundances. Recently, matrix–vector nonnegative tensor factorization (MV-NTF) has been successfully applied to ...
Yang Hu, Lei Sun, Ziyang Zhang, Feng Xie
doaj   +1 more source

Sparsity-Constrained NMF Algorithm Based on Evolution Strategy for Hyperspectral Unmixing

MATEC Web of Conferences, 2018
As a powerful and explainable blind separation tool, non-negative matrix factorization (NMF) is attracting increasing attention in Hyperspectral Unmixing(HU).
Ning ShangBin, Zuo FengChao
doaj   +1 more source

Spectral imaging in preclinical research and clinical pathology. [PDF]

, 2012
Spectral imaging methods are attracting increased interest from researchers and practitioners in basic science, pre-clinical and clinical arenas. A combination of better labeling reagents and better optics creates opportunities to detect and measure ...
Beechem, Joseph, Levenson, Richard, McNamara, George   +2 more
core