Abstract
A new fused Bayesian maximum entropy–variational analysis (BMEVA) method for enhanced radar/synthetic aperture radar (SAR) imaging is addressed as required for high-resolution remote sensing (RS) imagery. The variational analysis (VA) paradigm is adapted via incorporating the image gradient flow norm preservation into the overall reconstruction problem to control the geometrical properties of the desired solution. The metrics structure in the corresponding image representation and solution spaces is adjusted to incorporate the VA image formalism and RS model-level considerations; in particular, system calibration data and total image gradient flow power constraints. The BMEVA method aggregates the image model and system-level considerations into the fused SSP reconstruction strategy providing a regularized balance between the noise suppression and gained spatial resolution with the VA-controlled geometrical properties of the resulting solution. The efficiency of the developed enhanced radar imaging approach is illustrated through the numerical simulations with the real-world SAR imagery.
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References
Falkovich, S.E., Ponomaryov, V.I., Shkvarko, Y.V.: Optimal Reception of Space-Time bSignals in Channels with Scattering. Radio I Sviaz, Moscow (1989)
Wehner, D.R.: High-Resolution Radar, 2nd edn. Artech House, Boston (1994)
Henderson, F.M., Lewis, A.V.: Principles and Applications of Imaging Radar. In: Manual of Remote Sensing, 3rd edn., Wiley, New York (1998)
Shkvarko, Y.V.: Estimation of Wavefield Power Distribution in the Remotely Sensed Environment: Bayesian Maximum Entropy Approach. IEEE Transactions on Signal Processing 50, 2333–2346 (2002)
Shkvarko, Y.V.: Unifying Regularization and Bayesian Estimation Methods for Enhanced Imaging with Remotely Sensed Data. Part I - Theory. IEEE Transactions on Geoscience and Remote Sensing 42, 923–931 (2004)
Shkvarko, Y.V.: Unifying Regularization and Bayesian Estimation Methods for Enhanced Imaging with Remotely Sensed Data. Part II - Implementation and Performance Issues. IEEE Transactions on Geoscience and Remote Sensing 42, 932–940 (2004)
Black, M., Sapiro, G., Marimont, D.H., Hegger, D.: Robust Anisotropic Diffusion. IEEE Trans. Image Processing 7(3), 421–432 (1998)
Starck, J.L., Murtagh, F., Bijaoui, A.: Image Processing and Data Analysis: The Multiscale Approach. Cambridge University Press, Cambridge (1998)
Ben Hamza, A., Krim, H., Unal, B.G.: Unifying Probabilistic and Variational Estimation. IEEE Signal Processing Magazine 19, 37–47 (2002)
John, S., Vorontsov, M.: Multiframe Selective Information Fusion From Robust Error Estimation Theory. IEEE Trans. Image Processing 14(5), 577–584 (2005)
Barrett, H.H., Myers, K.J.: Foundations of Image Science. Wiley, New York (2004)
Vazquez-Bautista, R.F., Morales-Mendoza, L.J., Shkvarko, Y.V.: Aggregating the Statistical Estimation and Variational Analysis Methods in Radar Imagery. In: IGARSS. IEEE International Geoscience and Remote Sensing Symposium, Toulouse, France, vol. 3, pp. 2008–2010. IEEE, Los Alamitos (2003)
Erdogmus, D., Principe, J.C.: From Linear Adaptive Filtering to Nonlinear Information Processing. IEEE Signal Processing Magazine 23, 14–33 (2006)
Franceschetti, G., Iodice, A., Perna, S., Riccio, D.: Efficient Simulation of Airborne SAR Raw Data of Extended Scenes. IEEE Transactions on Geoscience and Remote Sensing 44, 2851–2860 (2006)
Morales-Mendoza, L.J., Vazquez-Bautista, R.F., Shkvarko, Y.V.: Unifying the Maximum Entropy and Variational Analysis Regularization Methods for Reconstruction of the Remote Sensing Imagery. IEEE Latin America Transactions 3, 60–73 (2005)
Space Imaging. In: GeoEye Inc. (2007), http://www.spaceimaging.com/quicklook
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Shkvarko, Y., Vazquez-Bautista, R., Villalon-Turrubiates, I. (2007). Fusion of Bayesian Maximum Entropy Spectral Estimation and Variational Analysis Methods for Enhanced Radar Imaging. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2007. Lecture Notes in Computer Science, vol 4678. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74607-2_10
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DOI: https://doi.org/10.1007/978-3-540-74607-2_10
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