Hyperspectral Subspace Identification Using SURE

Behnood Rasti, Magnus O. Ulfarsson*, Johannes R. Sveinsson

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)


The identification of the signal subspace is a very important first step for most hyperspectral algorithms. In this letter, we investigate the important problem of identifying the hyperspectral signal subspace by minimizing the mean squared error (MSE) between the true signal and an estimate of the signal. Since it is dependent on the true signal, the MSE is uncomputable in practice, and so we propose a method based on Stein's unbiased risk estimator that provides an unbiased estimate of the MSE. The resulting method is simple and fully automatic, and we evaluate it using both simulated and real hyperspectral data sets. Experimental results show that our proposed method compares well to recent state-of-the-art subspace identification methods.

Original languageEnglish
Article number7302055
Pages (from-to)2481-2485
Number of pages5
JournalIEEE Geoscience and Remote Sensing Letters
Issue number12
Publication statusPublished - Dec 2015

Bibliographical note

Publisher Copyright:
© 2004-2012 IEEE.

Other keywords

  • Data models
  • Estimation
  • Hyperspectral imaging
  • Noise
  • Tuning


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