Learning sparse codes for hyperspectral imagery

Adam S. Charles, Bruno A. Olshausen, Christopher J. Rozell

Research output: Contribution to journalArticlepeer-review

Abstract

The spectral features in hyperspectral imagery (HSI) contain significant structure that, if properly characterized, could enable more efficient data acquisition and improved data analysis. Because most pixels contain reflectances of just a few materials, we propose that a sparse coding model is well-matched to HSI data. Sparsity models consider each pixel as a combination of just a few elements from a larger dictionary, and this approach has proven effective in a wide range of applications. Furthermore, previous work has shown that optimal sparse coding dictionaries can be learned from a dataset with no other a priori information (in contrast to many HSI endmember discovery algorithms that assume the presence of pure spectra or side information). We modified an existing unsupervised learning approach and applied it to HSI data (with significant ground truth labeling) to learn an optimal sparse coding dictionary. Using this learned dictionary, we demonstrate three main findings: 1) the sparse coding model learns spectral signatures of materials in the scene and locally approximates nonlinear manifolds for individual materials; 2) this learned dictionary can be used to infer HSI-resolution data with very high accuracy from simulated imagery collected at multispectral-level resolution, and 3) this learned dictionary improves the performance of a supervised classification algorithm, both in terms of the classifier complexity and generalization from very small training sets.

Original languageEnglish (US)
Article number5762314
Pages (from-to)963-978
Number of pages16
JournalIEEE Journal on Selected Topics in Signal Processing
Volume5
Issue number5
DOIs
StatePublished - Sep 2011
Externally publishedYes

Keywords

  • Deblurring
  • dictionary learning
  • hyperspectral imagery (HSI)
  • inverse problems
  • material classification
  • multispectral imagery
  • remote sensing
  • sparse coding

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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