Joint segmentation and reconstruction of hyperspectral data with compressed measurements.

Published

Journal Article

This work describes numerical methods for the joint reconstruction and segmentation of spectral images taken by compressive sensing coded aperture snapshot spectral imagers (CASSI). In a snapshot, a CASSI captures a two-dimensional (2D) array of measurements that is an encoded representation of both spectral information and 2D spatial information of a scene, resulting in significant savings in acquisition time and data storage. The reconstruction process decodes the 2D measurements to render a three-dimensional spatio-spectral estimate of the scene and is therefore an indispensable component of the spectral imager. In this study, we seek a particular form of the compressed sensing solution that assumes spectrally homogeneous segments in the two spatial dimensions, and greatly reduces the number of unknowns, often turning the underdetermined reconstruction problem into one that is overdetermined. Numerical tests are reported on both simulated and real data representing compressed measurements.

Full Text

Duke Authors

Cited Authors

  • Zhang, Q; Plemmons, R; Kittle, D; Brady, D; Prasad, S

Published Date

  • August 2011

Published In

Volume / Issue

  • 50 / 22

Start / End Page

  • 4417 - 4435

PubMed ID

  • 21833118

Pubmed Central ID

  • 21833118

Electronic International Standard Serial Number (EISSN)

  • 1539-4522

International Standard Serial Number (ISSN)

  • 1559-128X

Digital Object Identifier (DOI)

  • 10.1364/ao.50.004417

Language

  • eng