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20112017
most citedSimultaneously sparse and low-rank abundance matrix estimation for hyperspectral image unmixing

137 citations · 283 across the 11 of their papers we have counts for

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cs.CV2017

Low-rank and Sparse NMF for Joint Endmembers' Number Estimation and Blind Unmixing of Hyperspectral Images

Paris V. Giampouras, Athanasios A. Rontogiannis, Konstantinos D. Koutroumbas

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…

cs.CV2015

Sparsity-aware Possibilistic Clustering Algorithms

Spyridoula D. Xenaki, Konstantinos D. Koutroumbas, Athanasios A. Rontogiannis

In this paper two novel possibilistic clustering algorithms are presented, which utilize the concept of sparsity. The first one, called sparse possibilistic c-means, exploits spars…

cs.CV2015★ 1 cited

On the convergence of the sparse possibilistic c-means algorithm

Spyridoula D. Xenaki, Konstantinos D. Koutroumbas, Athanasios A. Rontogiannis

In this paper, a convergence proof for the recently proposed sparse possibilistic c-means (SPCM) algorithm is provided, utilizing the celebrated Zangwill convergence theorem. It is…

cs.CV2015★ 137 cited

Simultaneously sparse and low-rank abundance matrix estimation for hyperspectral image unmixing

Paris Giampouras, Konstantinos Themelis, Athanasios Rontogiannis +1

In a plethora of applications dealing with inverse problems, e.g. in image processing, social networks, compressive sensing, biological data processing etc., the signal of interest…

cs.CV2014★ 63 cited

A Novel Adaptive Possibilistic Clustering Algorithm

Spyridoula D. Xenaki, Konstantinos D. Koutroumbas, Athanasios A. Rontogiannis

In this paper a novel possibilistic c-means clustering algorithm, called Adaptive Possibilistic c-means, is presented. Its main feature is that {\it all} its parameters, after thei…