By Topic

Integration of Spectral–Spatial Information for Hyperspectral Image Reconstruction From Compressive Random Projections

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Wei Li ; Center for Spatial Technol. & Remote Sensing, Univ. of California, Davis, Davis, CA, USA ; Prasad, S. ; Fowler, J.E.

Compressive-projection principal component analysis (CPPCA) has been developed to provide reconstruction from random projections of hyperspectral pixels and then subsequently extended by coupling it with classification such that the resulting class-dependent CPPCA yielded improved reconstruction performance. This letter provides an even greater integration of spatial and spectral information to further improve reconstruction performance. Specifically, instead of a pixel-based modulo partitioning employed by the original CPPCA sender, this work proposes an alternative block-based modulo partitioning, which preserves local spatial coherence; spatial segmentation is combined with the pixel-wise classification results using a majority voting rule at the receiver. Experimental results demonstrate not only improved reconstruction performance but also better detection of anomalies, as compared with previous approaches.

Published in:

Geoscience and Remote Sensing Letters, IEEE  (Volume:10 ,  Issue: 6 )