Spectral image processing using sparse linear transforms

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


We propose the employment of nonnegative sparse linear feature extraction as a tool for unsupervised spectral unmixing. Sparse feature extraction can be seen as a general linear unmixing approach that maps the data into a new dimensional space in which each of the components has only a limited number of non-zero values. Unlike other transforms that target decorrelation or statistical independence, our focus is on the enforcement of sparseness by imposing restrictions (such as cardinality or norm relationships), as well as nonnegativity. When compared to the linear mixing model, the sparse components can be naturally associated to the abundance of endmembers, and the inverse transform to the endmembers. Our approach is a variant of a well known technique based on Nonnegative Matrix Factorization (NMF). In most of the cases, the NMF components are produced using a gradient descent optimization algorithm that was previously shown to converge. To validate our approach we use quantitative (classification) and qualitative (visualization) analysis of hyperspectral data sets.

Original languageEnglish
Title of host publication2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009 - Proceedings
StatePublished - 2009
Event2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009 - Cape Town, South Africa
Duration: 12 Jul 200917 Jul 2009

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)


Other2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009
Country/TerritorySouth Africa
CityCape Town


  • Hyperspectral imagery
  • Linear mixing model
  • Sparse nonnegative matrix factorization


Dive into the research topics of 'Spectral image processing using sparse linear transforms'. Together they form a unique fingerprint.

Cite this