Reconstructions from Compressive Random Projections of Hyperspectral Imagery

Chapter
Part of the Augmented Vision and Reality book series (Augment Vis Real, volume 3)

Abstract

High-dimensional data such as hyperspectral imagery is traditionally acquired in full dimensionality before being reduced in dimension prior to processing. Conventional dimensionality reduction on-board remote devices is often prohibitive due to limited computational resources; on the other hand, integrating random projections directly into signal acquisition offers an alternative to explicit dimensionality reduction without incurring sender-side computational cost. Receiver-side reconstruction of hyperspectral data from such random projections in the form of compressive-projection principal component analysis (CPPCA) as well as compressed sensing (CS) is investigated. Specifically considered are single-task CS algorithms which reconstruct each hyperspectral pixel vector of a dataset independently as well as multi-task CS in which the multiple, possibly correlated hyperspectral pixel vectors are reconstructed simultaneously. These CS strategies are compared to CPPCA reconstruction which also exploits cross-vector correlations. Experimental results on popular AVIRIS datasets reveal that CPPCA outperforms various CS algorithms in terms of both squared-error as well as spectral-angle quality measures while requiring only a fraction of the computational cost.

Keywords

Hyperspectral data Principal component analysis (PCA) Random projections Rayleigh-Ritz theory 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringGeosystems Research Institute, Mississippi State UniversityMississippi StateUSA

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