 | Aguilera E. Synthetic aperture radar tomography compressed sensing models and algorithms: Diss. … Dr-Ing. / Deutsches Zentrum für Luft- und Raumfahrt, Institut für Hochfrequenztechnik und Radarsysteme, Oberpfaffenhofen. - Köln: DLR, 2014. - xvii, 120 p.: ill. - (Forschungsbericht; 2014-33). - Bibliogr.: p.109-120. - ISSN 1434-8454
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1 Introduction ................................................. 1
1.1 Motivation .............................................. 1
1.2 Previous Work ........................................... 6
1.3 Contribution ............................................ 7
1.4 Synopsis ............................................... 12
2 SAR Tomography Fundamentals ................................. 15
2.1 SAR Basics ............................................. 15
2.2 SAR Tomography ......................................... 21
2.3 Standard Sampling Considerations ....................... 24
2.4 Tomographic SAR Techniques ............................. 26
2.4.1 Fourier Beamforming ............................. 27
2.4.2 Capon Beamforming ............................... 27
3 Polarimetrie Forest Scattering .............................. 31
3.1 Polarimetrie SAR ....................................... 31
3.2 Forest Scattering Model ................................ 35
3.3 Analysis of Polarimetrie Signatures .................... 39
4 Sparsity-Based SAR Tomography ............................... 43
4.1 Compressed Sensing ..................................... 43
4.1.1 Single-Signal CS ................................ 43
4.1.2 Li Minimization in Action ....................... 45
4.1.3 Multi-Signal CS ................................. 49
4.2 CS for SAR Tomography .................................. 49
4.2.1 Single-Channel CS ............................... 49
4.2.2 Polarimetrie CS ................................. 51
4.3 Sparse Representations for Forested Areas .............. 54
4.3.1 Wavelet Systems ................................. 54
4.3.2 Wavelets for SAR Tomography ..................... 56
5 Separation of SMs via Convex Optimization ................... 59
6 Convexity Properties of Tomographic Techniques .............. 65
6.1 Single-Channel CS ...................................... 66
6.2 Polarimetrie CS ........................................ 67
6.3 Separation of SMs ...................................... 69
7 Experimental Results ........................................ 71
7.1 Sparsity-Based Experiments ............................. 71
7.1.1 Single-Channel CS ............................... 71
7.1.1.1 Experiments with Simulated Data ........ 71
7.1.1.2 Experiments with Real Data ............. 77
7.1.1.3 Computation Time ....................... 79
7.1.2 Polarimetrie CS ................................. 83
7.1.2.1 Tomographic Slices ..................... 83
7.1.2.2 Polarimetrie Validation ................ 90
7.2 Separation of SMs ...................................... 99
8 Conclusions and Recommendations ............................ 105
Bibliography .................................................. 109
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