Results 11 to 20 of about 9,466 (207)

Sparse Unmixing of Hyperspectral Data [PDF]

IEEE Transactions on Geoscience and Remote Sensing, 2011
Linear spectral unmixing is a popular tool in remotely sensed hyperspectral data interpretation. It aims at estimating the fractional abundances of pure spectral signatures (also called as endmembers) in each mixed pixel collected by an imaging spectrometer.
Marian-Daniel Iordache, José M. Bioucas-Dias, Antonio Plaza   +2 more
openaire   +3 more sources

Raman Microspectroscopy for Structural Indication in Ultrafast Laser Writing. [PDF]

Small Methods
Raman microspectroscopy is demonstrated as an in situ, phase‐specific probe for femtosecond laser fabrication in diamond. Multiple spectral indicators are systematically evaluated and correlated with electrical performance, establishing a robust methodology for process monitoring.
Cheng X, Picheo E, Chen Z, Booth MJ, Salter PS, Fernández-Galiana Á.   +5 more
europepmc   +2 more sources

Robust Double Spatial Regularization Sparse Hyperspectral Unmixing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021
With the help of endmember spectral library, sparse unmixing techniques have been successfully applied to hyperspectral image interpretation. The inclusion of spatial information in the sparse unmixing significantly improves the resulting fractional ...
Fan Li, Shaoquan Zhang, Chengzhi Deng, Bingkun Liang, Jingjing Cao, Shengqian Wang   +5 more
doaj   +1 more source

Multi-stage convolutional autoencoder network for hyperspectral unmixing

International Journal of Applied Earth Observations and Geoinformation, 2022
Hyperspectral unmixing (HU) is a fundamental and critical task in various hyperspectral image (HSI) applications. Over the past few years, the linear mixing model (LMM) has received widely attention for its high efficiency, definite physical meaning, and
Yang Yu, Yong Ma, Xiaoguang Mei, Fan Fan, Jun Huang, Hao Li   +5 more
doaj   +1 more source

A Label-Free Hyperspectral Imaging Device for Ex Vivo Characterization and Grading of Meningioma Tissues. [PDF]

J Biophotonics
HyperProbe1.1 enables rapid, label‐free biochemical mapping of freshly resected meningiomas. By quantifying endogenous biomarkers such as cytochrome c oxidase, hemoglobin derivatives, and lipids, the system reveals molecular signatures consistent with tumor grading and generates spatial maps that visualize metabolic and vascular heterogeneity across ...
Ricci P, Bonaudo C, Ezhov I, Toaha A, Nardini D, Camelia M, Lucidi F, Nozzoli F, Mach T, Tachtsidis I, Rueckert D, Della Puppa A, Giannoni L, Pavone FS.   +13 more
europepmc   +2 more sources

A Novel Collaborative Representation Algorithm for Spectral Unmixing of Hyperspectral Remotely Sensed Imagery

IEEE Access, 2021
Hyperspectral unmixing has attracted considerable attentions in recent years and some promising algorithms have been developed. In this paper, collaborative representation–based unmixing (CRU) for hyperspectral images is proposed.
Jing Wang
doaj   +1 more source

High-Content SRS Imaging Unveils Altered Cholesterol Metabolism in Ovarian Cancers Under CAR-T Treatment. [PDF]

Adv Sci (Weinh)
High‐content Stimulated Raman Scattering (SRS) Imaging reveals that ovarian cancer cells surviving Chimeric Antigen Receptor (CAR) ‐T cell challenge exhibit increased cholesterol esterification. Pharmacological inhibition of this pathway with Avasimibe significantly enhances CAR‐T induced killing of ovarian cancer cells by reducing cancer cell ...
Prabhu Dessai CV, Huang Z, Lin H, Ding G, He H, Siddiqui M, Zhang M, Wong W, Cheng JX.   +8 more
europepmc   +2 more sources

A New Deep Convolutional Network for Effective Hyperspectral Unmixing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022
Hyperspectral unmixing extracts pure spectral constituents (endmembers) and their corresponding abundance fractions from remotely sensed scenes. Most traditional hyperspectral unmixing methods require the results of other endmember extraction algorithms ...
Xuanwen Tao, Mercedes E. Paoletti, Lirong Han, Zhaoyue Wu, Peng Ren, Javier Plaza, Antonio Plaza, Juan M. Haut   +7 more
doaj   +1 more source

How Hyperspectral Image Unmixing and Denoising Can Boost Each Other

Remote Sensing, 2020
Hyperspectral linear unmixing and denoising are highly related hyperspectral image (HSI) analysis tasks. In particular, with the assumption of Gaussian noise, the linear model assumed for the HSI in the case of low-rank denoising is often the same as the
Behnood Rasti, Bikram Koirala, Paul Scheunders, Pedram Ghamisi   +3 more
doaj   +1 more source

Maximum Likelihood Estimation Based Nonnegative Matrix Factorization for Hyperspectral Unmixing

Remote Sensing, 2021
Hyperspectral unmixing (HU) is a research hotspot of hyperspectral remote sensing technology. As a classical HU method, the nonnegative matrix factorization (NMF) unmixing method can decompose an observed hyperspectral data matrix into the product of two
Qin Jiang, Yifei Dong, Jiangtao Peng, Mei Yan, Yi Sun   +4 more
doaj   +1 more source