James E. Fowler — Publications

J. E. Fowler and Q. Du, “Reconstructions from Compressive Random Projections of Hyperspectral Imagery,” in Optical Remote Sensing: Advances in Signal Processing and Exploitation Techniques, S. Prasad, L. M. Bruce, and J. Chanussot, Eds. Springer, 2011, ch. 3, pp. 31-48.
  • 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.

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