Unified Experiment Design, Bayesian Minimum Risk and Convex Projection Regularization Method for Enhanced Remote Sensing Imaging

  • Yuriy Shkvarko
  • Jose Tuxpan
  • Stewart Santos
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5856)

Abstract

We address new approach for enhanced multi-sensor imaging in uncertain remote sensing (RS) operational scenarios. Our approach is based on incorporating the projections onto convex solution sets (POCS) into the descriptive experiment design regularization (DEDR) and fused Bayesian regularization (FBR) methods to enhance the robustness and convergence of the overall unified DEDR/FBR-POCS procedure for enhanced RS imaging. Computer simulation examples are reported to illustrate the efficiency and improved operational performances of the proposed unified DEDR/FBR-POCS imaging techniques in the extremely uncertain RS operational scenarios.

Keywords

Convex sets descriptive regularization experiment design multi-sensor imaging remote sensing 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Yuriy Shkvarko
    • 1
  • Jose Tuxpan
    • 1
  • Stewart Santos
    • 1
  1. 1.Department of Electrical EngineeringCINVESTAV-IPNGuadalajaraMexico

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