Nonnegative Matrix Functional Factorization for Hyperspectral Unmixing With Nonuniform Spectral Sampling

Ting Wang, Jizhou Li, Michael K. Ng, Chao Wang*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review


Unmixing is a crucial technique in analyzing hyperspectral imaging (HSI) data, which involves identifying the endmembers present in the data and estimating their abundance maps. Due to some practical constraints in the atmospheric environment, HSI data is usually nonuniformly distributed along the spectral domain, which brings incomplete spectral information in the hyperspectral unmixing. To overcome this issue, we propose, in this article, nonnegative matrix functional factorization (NMFF) which is an extension of classical nonnegative matrix factorization (NMF) for hyperspectral unmixing. In particular, we present a novel functional factorization model by incorporating the implicit neural representations (INRs) to learn about endmembers. Our method effectively characterizes endmembers by learning a continuous representation through INR with positional encoding, capturing the nonuniform distribution of spectral wavelengths. This distinct approach streamlines NMFF’s iterative process for abundance extraction, bypassing the conventionally complex and cumbersome processing. When tested on various datasets, our hyperspectral unmixing approach consistently outperforms established techniques, showcasing the enhanced capabilities of our proposed model.

Original languageEnglish
Number of pages13
JournalIEEE Transactions on Geoscience and Remote Sensing
Publication statusE-pub ahead of print - 25 Dec 2023

Scopus Subject Areas

  • Electrical and Electronic Engineering
  • Earth and Planetary Sciences(all)

User-Defined Keywords

  • Hyperspectral unmixing
  • implicit neural representation (INR)
  • nonuniform sampling
  • nonnegative matrix factorization (NMF)
  • positional encoding
  • spectral domain


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