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Communication Dans Un Congrès Année : 2020

Pixel-wise linear/non linear nonnegative matrix factorization for unmixing of hyperspectral data

Fei Zhu
  • Fonction : Auteur
  • PersonId : 961917
Paul Honeine
Jie Chen
  • Fonction : Auteur
  • PersonId : 1004504

Résumé

Nonlinear spectral unmixing is a challenging and important task in hyperspectral image analysis. The kernel-based bi-objective nonnegative matrix factorization (Bi-NMF) has shown its usefulness in nonlinear unmixing; However, it suffers several issues that prohibit its practical application. In this work, we propose an unsupervised nonlinear unmixing method that overcomes these weaknesses. Specifically, the new method introduces into each pixel a parameter that adjusts the nonlinearity therein. These parameters are jointly optimized with endmembers and abundances, using a carefully designed objective function by multiplicative update rules. Experiments on synthetic and real datasets confirm the effectiveness of the proposed method.
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Dates et versions

hal-03088297 , version 1 (26-12-2020)

Identifiants

Citer

Fei Zhu, Paul Honeine, Jie Chen. Pixel-wise linear/non linear nonnegative matrix factorization for unmixing of hyperspectral data. ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 2020, Barcelona, Spain. pp.4737-4741, ⟨10.1109/ICASSP40776.2020.9053239⟩. ⟨hal-03088297⟩
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