James E. Fowler — Publications

C. Chen, W. Li, E. W. Tramel, and J. E. Fowler, “Reconstruction of Hyperspectral Imagery from Random Projections Using Multihypothesis Prediction,” IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 1, pp. 365-374, January 2014.
  • Abstract:
    Reconstruction of hyperspectral imagery from spectral random projections is considered. Specifically, multiple predictions drawn for a pixel vector of interest are made from spatially neighboring pixel vectors within an initial non-predicted reconstruction. A two-phase hypothesis-generation procedure based on partitioning and merging of spectral bands according to the correlation coefficients between bands is proposed to fine-tune the hypotheses. The resulting prediction is used to generate a residual in the projection domain. This residual being typically more compressible than the original pixel vector leads to improved reconstruction quality. To appropriately weight the hypothesis predictions, a distance-weighted Tikhonov regularization to an ill-posed least-squares optimization is proposed. Experimental results demonstrate that the proposed reconstruction significantly outperforms alternative strategies not employing multihypothesis prediction.

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