SAR Object Recognition Based on Multi-band and Multi-polarization Simulation Image

doi:10.16182/j.issn1004731x.joss.201710032

Abstract

Abstract: The object model was built based on Creator, and object texture-material mapping was performed by Vega TMM tool. The multi-band and multi-polarization SAR image database was built by visual simulation technology. A hybrid intelligent optimization algorithm was designed to optimize combination of band and polarization by genetic algorithm and binary particle optimization. Zernike moment features, Gabor wavelet coefficients, etc were extracted from original image and rectified image to make up of feature candidates, and the feature selection experiments were carried out by using multi-band and multi-polarization SAR images. Simulation results demonstrate that, building SAR image database through simulation technology is an effective method to perform research on multi-band and multi-polarization SAR image recognition; target recognition rate can be improved for multi-band and multi-polarization SAR images using the optimized feature set.

Key words: SAR, multi-band and multi-polarization, computer simulation, feature selection

CLC Number:

TP753

Gu Yu, Zhang Qin, Xu Ying. SAR Object Recognition Based on Multi-band and Multi-polarization Simulation Image[J]. Journal of System Simulation, 2017, 29(10): 2482-2488.

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