James E. Fowler — Publications

  • W. Li, S. Prasad, and J. E. Fowler, “Integration of Spectral-Spatial Information for Hyperspectral Image Reconstruction from Compressive Random Projections,” IEEE Geoscience and Remote Sensing Letters, vol. 10, no. 6, pp. 1379-1383, November 2013.
    • Abstract:
      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.

    • Text:
      Adobe PDF Format

    © 2013 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

    This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.