Results 61 to 70 of about 9,243 (189)
Fast and Robust Recursive Algorithms for Separable Nonnegative Matrix Factorization [PDF]
, 2013 In this paper, we study the nonnegative matrix factorization problem under the separability assumption (that is, there exists a cone spanned by a small subset of the columns of the input nonnegative data matrix containing all columns), which is ...
Gillis, Nicolas, Vavasis, Stephen A.
core +2 more sources
Crossing scales and eras: Correlative multimodal microscopy heritage studies
Journal of Microscopy, Volume 301, Issue 1, Page 40-68, January 2026.Abstract The comprehensive characterisation of complex, irreplaceable cultural heritage artefacts presents significant challenges for traditional analytical methods, which can fall short in providing multi‐scale, non‐invasive analysis. Correlative Multimodal Microscopy (CoMic), an approach that integrates data from multiple techniques, offers a ...
Charles Wood +3 more
wiley +1 more source
Generalized linear mixing model accounting for endmember variability
, 2017 Endmember variability is an important factor for accurately unveiling vital information relating the pure materials and their distribution in hyperspectral images.
Bermudez, José Carlos Moreira +2 more
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Self-Dictionary Sparse Regression for Hyperspectral Unmixing: Greedy Pursuit and Pure Pixel Search are Related [PDF]
, 2015 This paper considers a recently emerged hyperspectral unmixing formulation based on sparse regression of a self-dictionary multiple measurement vector (SD-MMV) model, wherein the measured hyperspectral pixels are used as the dictionary.
Bioucas-Dias, José M. +3 more
core +2 more sources
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
Superpixel-Guided Matrix-Valued Kernel Functions for Multiscale Nonlinear Hyperspectral Unmixing
IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingHyperspectral 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
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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 +3 more
doaj +1 more source
A Sparse Topic Relaxion and Group Clustering Model for Hyperspectral Unmixing
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Hyperspectral unmixing (HU) has been a hot research topic in the field of hyperspectral remote sensing. In recent years, the employment of the probabilistic topic model to acquire the latent topics of hyperspectral images has been an effective method for
Qiqi Zhu +4 more
doaj +1 more source
Approximate Sparse Regularized Hyperspectral Unmixing [PDF]
Mathematical Problems in Engineering, 2014 Sparse regression based unmixing has been recently proposed to estimate the abundance of materials present in hyperspectral image pixel. In this paper, a novel sparse unmixing optimization model based on approximate sparsity, namely, approximate sparse unmixing (ASU), is firstly proposed to perform the unmixing task for hyperspectral remote sensing ...
Chengzhi Deng +6 more
openaire +1 more source
Gradient Type Methods for Linear Hyperspectral Unmixing
CSIAM Transactions on Applied Mathematics, 2022 Summary: Hyperspectral unmixing (HU) plays an important role in terrain classification, agricultural monitoring, mineral recognition and quantification, and military surveillance. The existing model of the linear HU requires the observed vector to be a linear combination of the vertices.
Xu, Fangfang +4 more
openaire +3 more sources