Distributed compressed sensing of Hyperspectral images via blind source separation

Golbabaee, Mohammad; Arberet, Simon

doi:10.1109/acssc.2010.5757497

Cited by 15 publications

(10 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Candès, Tao, and Donoho (Candès & Tao, ; Donoho, ; Candès & Wakin, ) have posed that, given knowledge on the signal's sparsity, the original signal may still be reconstructed with fewer samples than the sampling theorem technically requires. Compressed sensing has been applied in various domains such as image reconstruction (Mairal, Elad, & Sapiro, ), hyperspectral imaging (Golbabaee, Arberet & Vandergheynst, ), and clustering (Guillermo, Sprechmann, & Sapiro, ). In the case of IMS, this would enable a reduction in the number of samples (pixels) collected in tissue, and enable the computational processing of smaller datasets.…”

Section: Factorizationmentioning

confidence: 99%

Unsupervised machine learning for exploratory data analysis in imaging mass spectrometry

Verbeeck

Caprioli

Plas

2019

Mass Spectrometry Reviews

200

190

View full text Add to dashboard Cite

Imaging mass spectrometry (IMS) is a rapidly advancing molecular imaging modality that can map the spatial distribution of molecules with high chemical specificity. IMS does not require prior tagging of molecular targets and is able to measure a large number of ions concurrently in a single experiment. While this makes it particularly suited for exploratory analysis, the large amount and high‐dimensional nature of data generated by IMS techniques make automated computational analysis indispensable. Research into computational methods for IMS data has touched upon different aspects, including spectral preprocessing, data formats, dimensionality reduction, spatial registration, sample classification, differential analysis between IMS experiments, and data‐driven fusion methods to extract patterns corroborated by both IMS and other imaging modalities. In this work, we review unsupervised machine learning methods for exploratory analysis of IMS data, with particular focus on (a) factorization, (b) clustering, and (c) manifold learning. To provide a view across the various IMS modalities, we have attempted to include examples from a range of approaches including matrix assisted laser desorption/ionization, desorption electrospray ionization, and secondary ion mass spectrometry‐based IMS. This review aims to be an entry point for both (i) analytical chemists and mass spectrometry experts who want to explore computational techniques; and (ii) computer scientists and data mining specialists who want to enter the IMS field. © 2019 The Authors. Mass Spectrometry Reviews published by Wiley Periodicals, Inc. Mass SpecRev 00:1–47, 2019.

show abstract

Section: Factorizationmentioning

confidence: 99%

Unsupervised machine learning for exploratory data analysis in imaging mass spectrometry

Verbeeck

Caprioli

Plas

2019

Mass Spectrometry Reviews

200

190

View full text Add to dashboard Cite

show abstract

“…In addition, in a recent work [37], the authors further impose a low-rank constraint on the coefficient matrix S for the demixing task. Further, applications of compressive sampling have been explored in [38], while [5] analyzes the case where HS images are noisy and incomplete. The techniques discussed above focus on identifying all materials in a given HS image.…”

Section: Prior Artmentioning

confidence: 99%

A Dictionary-Based Generalization of Robust PCA With Applications to Target Localization in Hyperspectral Imaging

Rambhatla

Ren

et al. 2020

IEEE Trans. Signal Process.

View full text Add to dashboard Cite

We consider the task of localizing targets of interest in a hyperspectral (HS) image based on their spectral signature(s), by posing the problem as two distinct convex demixing task(s). With applications ranging from remote sensing to surveillance, this task of target detection leverages the fact that each material/object possesses its own characteristic spectral response, depending upon its composition. However, since signatures of different materials are often correlated, matched filtering-based approaches may not be apply here. To this end, we model a HS image as a superposition of a low-rank component and a dictionary sparse component, wherein the dictionary consists of the a priori known characteristic spectral responses of the target we wish to localize, and develop techniques for two different sparsity structures, resulting from different model assumptions. We also present the corresponding recovery guarantees, leveraging our recent theoretical results from a companion paper. Finally, we analyze the performance of the proposed approach via experimental evaluations on real HS datasets for a classification task, and compare its performance with related techniques.Index Terms-Hyperspectral imaging, Robust-PCA, dictionary sparse, target localization, and remote sensing.

show abstract

“…In addition, in a recent work [27], the authors further impose a low-rank constraint on the coefficient matrix A. Further, applications of compressive sampling have been explored in [28], while [3] analyzes the case where HS images are noisy and incomplete.…”

Section: B Prior Artmentioning

confidence: 99%

Target-based hyperspectral demixing via generalized robust PCA

Rambhatla

Haupt

2017

2017 51st Asilomar Conference on Signals, Systems, and Computers

View full text Add to dashboard Cite

Localizing targets of interest in a given hyperspectral (HS) image has applications ranging from remote sensing to surveillance. This task of target detection leverages the fact that each material/object possesses its own characteristic spectral response, depending upon its composition. As signatures of different materials are often correlated, matched filtering based approaches may not be appropriate in this case. In this work, we present a technique to localize targets of interest based on their spectral signatures. We also present the corresponding recovery guarantees, leveraging our recent theoretical results. To this end, we model a HS image as a superposition of a lowrank component and a dictionary sparse component, wherein the dictionary consists of the a priori known characteristic spectral responses of the target we wish to localize. Finally, we analyze the performance of the proposed approach via experimental validation on real HS data for a classification task, and compare it with related techniques.Index Terms-Hyperspectral imaging, Robust-PCA, dictionary sparse, target localization, and remote sensing.

show abstract

Distributed compressed sensing of Hyperspectral images via blind source separation

Cited by 15 publications

References 9 publications

Unsupervised machine learning for exploratory data analysis in imaging mass spectrometry

Unsupervised machine learning for exploratory data analysis in imaging mass spectrometry

A Dictionary-Based Generalization of Robust PCA With Applications to Target Localization in Hyperspectral Imaging

Target-based hyperspectral demixing via generalized robust PCA

Contact Info

Product

Resources

About