1. An Interferometric Coherence Optimization Method Based on Genetic Algorithm in PolInSAR Peifeng Ma, Hong Zhang, Chao Wang , Jiehong Chen Center for Earth Observation and Digital Earth Chinese Academy of Sciences [email_address] Vancouver, Canada July 29, 2011 IGRASS2011
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4. Introduction of coherence optimization scattering matrix: scattering vector: generalized vector expression for the coherence: The accuracy of height estimation depends on the quality of interferogram, the indicator of which is complex coherence. We are always attempting to search for the best projection vector combination to acquire the highest interferometric coherence.
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8. Coherence optimization with GA scattering mechanism definition: Each individual of population has six chromosomes to be developed in single-mechanism: When optimizing the second coherence we must add a constraint: So the last two chromosomes can be represented by the first four as:
9. Coherence optimization with GA When optimizing the third coherence we must add another constraint: and So the last four chromosomes can be represented by the first two as: where
10. Coherence optimization with GA Block diagram of coherence optimization using GA: Pre-processing Initialization Genetic operation Output
11. Experiment results The data we choose is Chinese X-band airborne PolInSAR data over Sanya area: Optical image from Google Earth and Pauli image
12. Experiment results Initialization: Population size: 50 Terminating generation: 100 Crossover probability: 0.9 Mutation probability: 0.1 the interval of : [-1,1] Precision: 0.001 We select one pixel to demonstrate the process of tendency to stability as shown in right. Initialized and evolutional coherence
13. Experiment results Mean of coherence in different optimization methods (L=9) The optimum coherence and relative phase Histograms of the optimum coherence C&P 0.887 0.776 0.602 GA 0.872 0.777 0.622 Colin 0.854 0.791 0.703 GD 0.776 0.646 0.481
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