Journal ArticleDOI
Spectral-Spatial Hyperspectral Unmixing Using Nonnegative Matrix Factorization
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TLDR
This article builds up another hyperspectral unmixing technique named spectral–spatial weighted sparse NMF (SSWNMF), in which two weighting factors are acquainted into the NMF model to upgrade the sparsity of the solution and capture the piecewise smooth structure of the data.Abstract:
Remotely sensed hyperspectral images contain several bands (at about adjoining frequencies) for a similar zone on the surface of the Earth. Hyperspectral unmixing is a significant method for breaking down hyperspectral images into the components (endmembers) that conform each (potentially mixed) pixel and their abundance maps. Nonnegative matrix factorization (NMF) has attracted huge consideration because of the way that it can address mixed pixel scenarios. Most existing NMF unmixing techniques do not include spatial information in the analysis. An ongoing trend is to fuse the spatial and the spectral information contained in hyperspectral scenes to improve the solution. In this article, we build up another hyperspectral unmixing technique named spectral-spatial weighted sparse NMF (SSWNMF), in which two weighting factors are acquainted into the NMF model to upgrade the sparsity of the solution and capture the piecewise smooth structure of the data. We adopt a multiplicative iterative strategy to implement the proposed SSWNMF model. Our experimental results, conducted with both synthetic and real hyperspectral data, uncover that the proposed SSWNMF strategy can get accurate unmixing results over those gave by other unmixing strategies, with less parameter tuning.read more
Citations
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Journal ArticleDOI
Hyperspectral Pixel Unmixing with Latent Dirichlet Variational Autoencoder
TL;DR: In this paper , the authors compute the infrared divergent contribution to the real part of the two-loop eikonal and from it derive the radiative contribution of the deflection angle for spins aligned to the orbital angular momentum, the loss of angular momentum and the zero frequency limit of the energy spectrum for any spin.
Journal ArticleDOI
A Multiobjective Method Leveraging Spatial–Spectral Relationship for Hyperspectral Unmixing
TL;DR: In this paper , a graph-based multiobjective optimization method for sparse unmixing (GMoSU) is proposed to exploit the spatial spectral relationship of hyperspectral images.
Proceedings ArticleDOI
Spatial Graph Regularized Nonnegative Matrix Factorization for Hyperspectral Unmixing
TL;DR: In this article , a spatial graph regularized nonnegative matrix factorization unmixing framework (SGNMF) is established, where the graph regularization is introduced to characterize the latent manifold structure of the data, and the spatial weighting factor is used to mine the spatial correlation between pixels.
Journal ArticleDOI
Weighted Residual NMF With Spatial Regularization for Hyperspectral Unmixing
Taner Ince,Nicolas Dobigeon +1 more
TL;DR: In this paper , a weighted residual nonnegative matrix factorization (NMF) with spatial regularization is proposed to unmix hyperspectral (HS) data, which treats each row of the residual based on the weighting factor.
Journal ArticleDOI
Abundance Estimation Based on Band Fusion and Prioritization Mechanism
TL;DR: This article proposes a new band processing approach for abundance estimation, to be called sequential band fusion (SBF), and provides a detailed theoretical description and formula derivation of the SBF and combines it with band sequence, RP, and SP to propose three different fusion mechanisms.
References
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Journal ArticleDOI
Learning the parts of objects by non-negative matrix factorization
TL;DR: An algorithm for non-negative matrix factorization is demonstrated that is able to learn parts of faces and semantic features of text and is in contrast to other methods that learn holistic, not parts-based, representations.
Learning parts of objects by non-negative matrix factorization
TL;DR: In this article, non-negative matrix factorization is used to learn parts of faces and semantic features of text, which is in contrast to principal components analysis and vector quantization that learn holistic, not parts-based, representations.
Proceedings Article
Algorithms for Non-negative Matrix Factorization
Daniel D. Lee,H. Sebastian Seung +1 more
TL;DR: Two different multiplicative algorithms for non-negative matrix factorization are analyzed and one algorithm can be shown to minimize the conventional least squares error while the other minimizes the generalized Kullback-Leibler divergence.
Journal ArticleDOI
Enhancing Sparsity by Reweighted ℓ 1 Minimization
TL;DR: A novel method for sparse signal recovery that in many situations outperforms ℓ1 minimization in the sense that substantially fewer measurements are needed for exact recovery.
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