Low-rank and sparse NMF for joint endmembers' number estimation and blind unmixing of hyperspectral images. Giampouras, P. V., Rontogiannis, A. A., & Koutroumbas, K. D. In 2017 25th European Signal Processing Conference (EUSIPCO), pages 1430-1434, Aug, 2017.
![Low-rank and sparse NMF for joint endmembers' number estimation and blind unmixing of hyperspectral images [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
Paper doi abstract bibtex
Paper doi abstract bibtex Estimation of the number of endmembers existing in a scene constitutes a critical task in the hyperspectral unmixing process. The accuracy of this estimate plays a crucial role in subsequent unsupervised unmixing steps i.e., the derivation of the spectral signatures of the endmembers (endmembers' extraction) and the estimation of the abundance fractions of the pixels. A common practice amply followed in literature is to treat endmembers' number estimation and unmixing, independently as two separate tasks, providing the outcome of the former as input to the latter. In this paper, we go beyond this computationally demanding strategy. More precisely, we set forth a multiple constrained optimization framework, which encapsulates endmembers' number estimation and unsuper-vised unmixing in a single task. This is attained by suitably formulating the problem via a low-rank and sparse nonnegative matrix factorization rationale, where low-rankness is promoted with the use of a sophisticated ℓ1/ℓ2 norm penalty term. An alternating proximal algorithm is then proposed for minimizing the emerging cost function. The results obtained by simulated and real data experiments verify the effectiveness of the proposed approach.
@InProceedings{8081445,
author = {P. V. Giampouras and A. A. Rontogiannis and K. D. Koutroumbas},
booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},
title = {Low-rank and sparse NMF for joint endmembers' number estimation and blind unmixing of hyperspectral images},
year = {2017},
pages = {1430-1434},
abstract = {Estimation of the number of endmembers existing in a scene constitutes a critical task in the hyperspectral unmixing process. The accuracy of this estimate plays a crucial role in subsequent unsupervised unmixing steps i.e., the derivation of the spectral signatures of the endmembers (endmembers' extraction) and the estimation of the abundance fractions of the pixels. A common practice amply followed in literature is to treat endmembers' number estimation and unmixing, independently as two separate tasks, providing the outcome of the former as input to the latter. In this paper, we go beyond this computationally demanding strategy. More precisely, we set forth a multiple constrained optimization framework, which encapsulates endmembers' number estimation and unsuper-vised unmixing in a single task. This is attained by suitably formulating the problem via a low-rank and sparse nonnegative matrix factorization rationale, where low-rankness is promoted with the use of a sophisticated ℓ1/ℓ2 norm penalty term. An alternating proximal algorithm is then proposed for minimizing the emerging cost function. The results obtained by simulated and real data experiments verify the effectiveness of the proposed approach.},
keywords = {hyperspectral imaging;image processing;matrix decomposition;optimisation;sparse matrices;blind unmixing;hyperspectral unmixing process;low-rankness;unsupervised unmixing;subsequent unsupervised unmixing steps;nonnegative matrix factorization rationale;alternating proximal algorithm;cost function;norm penalty term;Signal processing algorithms;Minimization;Estimation;Cost function;Hyperspectral imaging;NMF;sparse and low-rank;number of endmembers;unsupervised unmixing},
doi = {10.23919/EUSIPCO.2017.8081445},
issn = {2076-1465},
month = {Aug},
url = {https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570347555.pdf},
}Downloads: 0
{"_id":"PhEBLA3PYQPwJj9Nf","bibbaseid":"giampouras-rontogiannis-koutroumbas-lowrankandsparsenmfforjointendmembersnumberestimationandblindunmixingofhyperspectralimages-2017","authorIDs":[],"author_short":["Giampouras, P. V.","Rontogiannis, A. A.","Koutroumbas, K. D."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["P.","V."],"propositions":[],"lastnames":["Giampouras"],"suffixes":[]},{"firstnames":["A.","A."],"propositions":[],"lastnames":["Rontogiannis"],"suffixes":[]},{"firstnames":["K.","D."],"propositions":[],"lastnames":["Koutroumbas"],"suffixes":[]}],"booktitle":"2017 25th European Signal Processing Conference (EUSIPCO)","title":"Low-rank and sparse NMF for joint endmembers' number estimation and blind unmixing of hyperspectral images","year":"2017","pages":"1430-1434","abstract":"Estimation of the number of endmembers existing in a scene constitutes a critical task in the hyperspectral unmixing process. The accuracy of this estimate plays a crucial role in subsequent unsupervised unmixing steps i.e., the derivation of the spectral signatures of the endmembers (endmembers' extraction) and the estimation of the abundance fractions of the pixels. A common practice amply followed in literature is to treat endmembers' number estimation and unmixing, independently as two separate tasks, providing the outcome of the former as input to the latter. In this paper, we go beyond this computationally demanding strategy. More precisely, we set forth a multiple constrained optimization framework, which encapsulates endmembers' number estimation and unsuper-vised unmixing in a single task. This is attained by suitably formulating the problem via a low-rank and sparse nonnegative matrix factorization rationale, where low-rankness is promoted with the use of a sophisticated ℓ1/ℓ2 norm penalty term. An alternating proximal algorithm is then proposed for minimizing the emerging cost function. The results obtained by simulated and real data experiments verify the effectiveness of the proposed approach.","keywords":"hyperspectral imaging;image processing;matrix decomposition;optimisation;sparse matrices;blind unmixing;hyperspectral unmixing process;low-rankness;unsupervised unmixing;subsequent unsupervised unmixing steps;nonnegative matrix factorization rationale;alternating proximal algorithm;cost function;norm penalty term;Signal processing algorithms;Minimization;Estimation;Cost function;Hyperspectral imaging;NMF;sparse and low-rank;number of endmembers;unsupervised unmixing","doi":"10.23919/EUSIPCO.2017.8081445","issn":"2076-1465","month":"Aug","url":"https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570347555.pdf","bibtex":"@InProceedings{8081445,\n author = {P. V. Giampouras and A. A. Rontogiannis and K. D. Koutroumbas},\n booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},\n title = {Low-rank and sparse NMF for joint endmembers' number estimation and blind unmixing of hyperspectral images},\n year = {2017},\n pages = {1430-1434},\n abstract = {Estimation of the number of endmembers existing in a scene constitutes a critical task in the hyperspectral unmixing process. The accuracy of this estimate plays a crucial role in subsequent unsupervised unmixing steps i.e., the derivation of the spectral signatures of the endmembers (endmembers' extraction) and the estimation of the abundance fractions of the pixels. A common practice amply followed in literature is to treat endmembers' number estimation and unmixing, independently as two separate tasks, providing the outcome of the former as input to the latter. In this paper, we go beyond this computationally demanding strategy. More precisely, we set forth a multiple constrained optimization framework, which encapsulates endmembers' number estimation and unsuper-vised unmixing in a single task. This is attained by suitably formulating the problem via a low-rank and sparse nonnegative matrix factorization rationale, where low-rankness is promoted with the use of a sophisticated ℓ1/ℓ2 norm penalty term. An alternating proximal algorithm is then proposed for minimizing the emerging cost function. The results obtained by simulated and real data experiments verify the effectiveness of the proposed approach.},\n keywords = {hyperspectral imaging;image processing;matrix decomposition;optimisation;sparse matrices;blind unmixing;hyperspectral unmixing process;low-rankness;unsupervised unmixing;subsequent unsupervised unmixing steps;nonnegative matrix factorization rationale;alternating proximal algorithm;cost function;norm penalty term;Signal processing algorithms;Minimization;Estimation;Cost function;Hyperspectral imaging;NMF;sparse and low-rank;number of endmembers;unsupervised unmixing},\n doi = {10.23919/EUSIPCO.2017.8081445},\n issn = {2076-1465},\n month = {Aug},\n url = {https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570347555.pdf},\n}\n\n","author_short":["Giampouras, P. V.","Rontogiannis, A. A.","Koutroumbas, K. D."],"key":"8081445","id":"8081445","bibbaseid":"giampouras-rontogiannis-koutroumbas-lowrankandsparsenmfforjointendmembersnumberestimationandblindunmixingofhyperspectralimages-2017","role":"author","urls":{"Paper":"https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570347555.pdf"},"keyword":["hyperspectral imaging;image processing;matrix decomposition;optimisation;sparse matrices;blind unmixing;hyperspectral unmixing process;low-rankness;unsupervised unmixing;subsequent unsupervised unmixing steps;nonnegative matrix factorization rationale;alternating proximal algorithm;cost function;norm penalty term;Signal processing algorithms;Minimization;Estimation;Cost function;Hyperspectral imaging;NMF;sparse and low-rank;number of endmembers;unsupervised unmixing"],"metadata":{"authorlinks":{}},"downloads":0},"bibtype":"inproceedings","biburl":"https://raw.githubusercontent.com/Roznn/EUSIPCO/main/eusipco2017url.bib","creationDate":"2021-02-13T16:38:25.674Z","downloads":0,"keywords":["hyperspectral imaging;image processing;matrix decomposition;optimisation;sparse matrices;blind unmixing;hyperspectral unmixing process;low-rankness;unsupervised unmixing;subsequent unsupervised unmixing steps;nonnegative matrix factorization rationale;alternating proximal algorithm;cost function;norm penalty term;signal processing algorithms;minimization;estimation;cost function;hyperspectral imaging;nmf;sparse and low-rank;number of endmembers;unsupervised unmixing"],"search_terms":["low","rank","sparse","nmf","joint","endmembers","number","estimation","blind","unmixing","hyperspectral","images","giampouras","rontogiannis","koutroumbas"],"title":"Low-rank and sparse NMF for joint endmembers' number estimation and blind unmixing of hyperspectral images","year":2017,"dataSources":["2MNbFYjMYTD6z7ExY","uP2aT6Qs8sfZJ6s8b"]}