Reconstructing and segmenting hyperspectral images from compressed measurements

Qiang Zhang, Robert Plemmons, David Kittle, David Brady, Sudhakar Prasad

Research output: Contribution to journalConference articlepeer-review

8 Scopus citations


A joint reconstruction and segmentation model for hyperspectral data obtained from a compressive measurement system is proposed, and some preliminary tests are described. Although hyperspectral imaging (HSI) technology has incredible potential, its utility is currently limited because of the quantity and complexity of the data it gathers. Yet, often the scene to be reconstructed from the HSI data contains far less information, typically consisting of spectrally and spatially homogeneous segments that can be represented sparsely in an appropriate basis. Such vast informational redundancy thus implicitly contained in the HSI data warrants a compressed sensing (CS) strategy that acquires appropriately coded spectral-spatial data from which one can reconstruct the original image more efficiently, while still enabling target identification procedures. A coded-aperture snapshot spectral imager (CASSI) is considered here, and a joint reconstruction and segmentation model for data obtained from CASSI compressive measurements is proposed and preliminary numerical experiments are presented.

Original languageEnglish (US)
Article number6080939
JournalWorkshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing
StatePublished - 2011
Externally publishedYes
Event3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2011 - Lisbon, Portugal
Duration: Jun 6 2011Jun 9 2011


  • Hyperspectral data
  • compressive measurements
  • reconstruction
  • segmentation
  • target recognition

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing


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