Results 41 to 50 of about 9,243 (189)

Clustered Multitask Nonnegative Matrix Factorization for Spectral Unmixing of Hyperspectral Data

, 2019
In this paper, the new algorithm based on clustered multitask network is proposed to solve spectral unmixing problem in hyperspectral imagery. In the proposed algorithm, the clustered network is employed.
Khoshsokhan, Sara, Rajabi, Roozbeh, Zayyani, Hadi   +2 more
core   +1 more source

Pixel-Level and Global Similarity-Based Adversarial Autoencoder Network for Hyperspectral Unmixing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Hyperspectral unmixing is a critical task in remote sensing, enabling the decomposition of hyperspectral data into their constituent endmembers and abundances.
Wei Tao, Haiyang Zhang, Shan Zeng, Long Wang, Chaoxian Liu, Bing Li   +5 more
doaj   +1 more source

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   +1 more source

Deep Learning‐Assisted Coherent Raman Scattering Microscopy

Advanced Intelligent Discovery, EarlyView.
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

Spectral mixture analysis of EELS spectrum-images [PDF]

, 2012
Recent advances in detectors and computer science have enabled the acquisition and the processing of multidimensional datasets, in particular in the field of spectral imaging.
Brun, Nathalie, Dobigeon, Nicolas
core   +3 more sources

HYPERSPECTRAL IMAGE RESOLUTION ENHANCEMENT BASED ON SPECTRAL UNMIXING AND INFORMATION FUSION [PDF]

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2012
Hyperspectral imaging sensors exibit high spectral resolution, but normally low spatial resolution. This leads to spectral signatures of pixels originating from different object types. Such pixels are called mixed pixels. Spectral unmixing methods can be
J. Bieniarz, D. Cerra, J. Avbelj, P. Reinartz, R. Müller   +4 more
doaj   +1 more source

A Label‐Free Hyperspectral Imaging Device for Ex Vivo Characterization and Grading of Meningioma Tissues

Journal of Biophotonics, EarlyView.
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 ...
Pietro Ricci, Camilla Bonaudo, Ivan Ezhov, Anam Toaha, Dorotea Nardini, Manuel Camelia, Federica Lucidi, Filippo Nozzoli, Tim Mach, Ilias Tachtsidis, Daniel Rueckert, Alessandro Della Puppa, Luca Giannoni, Francesco S. Pavone   +13 more
wiley   +1 more source

Non-convex regularization in remote sensing [PDF]

, 2016
In this paper, we study the effect of different regularizers and their implications in high dimensional image classification and sparse linear unmixing. Although kernelization or sparse methods are globally accepted solutions for processing data in high ...
Barlaud, Michel, Flamary, Remi, Tuia, Devis   +2 more
core   +4 more sources

Hyperspectral Unmixing Network Accounting for Spectral Variability Based on a Modified Scaled and a Perturbed Linear Mixing Model

Remote Sensing, 2023
Spectral unmixing is one of the prime topics in hyperspectral image analysis, as images often contain multiple sources of spectra. Spectral variability is one of the key factors affecting unmixing accuracy, since spectral signatures are affected by ...
Ying Cheng, Liaoying Zhao, Shuhan Chen, Xiaorun Li   +3 more
doaj   +1 more source

Robust Hyperspectral Unmixing With Correntropy-Based Metric

IEEE Transactions on Image Processing, 2015
Hyperspectral unmixing is one of the crucial steps for many hyperspectral applications. The problem of hyperspectral unmixing has proven to be a difficult task in unsupervised work settings where the endmembers and abundances are both unknown. What is more, this task becomes more challenging in the case that the spectral bands are degraded with noise ...
Wang, Ying, Pan, Chunhong, Xiang, Shiming, Zhu, Feiyun   +3 more
openaire   +3 more sources