Pixel-wise linear/non linear nonnegative matrix factorization for unmixing of hyperspectral data - Normandie Université Access content directly
Conference Papers Year : 2020

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

Fei Zhu
  • Function : Author
  • PersonId : 961917
Paul Honeine
Jie Chen
  • Function : Author
  • PersonId : 1004504

Abstract

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.
Fichier principal
Vignette du fichier
20.icassp.hype.pdf (478.75 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

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

Identifiers

Cite

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⟩
23 View
133 Download

Altmetric

Share

Gmail Facebook X LinkedIn More