Non-blind Source KPCA Residual Noise Ratio Threshold TomoSAR Imaging Method
Synthetic aperture radar(SAR)tomography is an advanced 3D imaging technology based on interferometric SAR.The tomographic SAR(TomoSAR)is realized by separating the scatterers in the same range-azimuth resolution unit by the third dimen-sional inversion technique.Therefore,the scatterers separation in the same range-azimuth resolution element is the key of Tomo-SAR.In this paper,a non-blind scatterer separation algorithm is proposed,which combines kernel-principal component analysis(KPCA)and residual noise ratio threshold to estimate the number of scatterers in the same range-azimuth pixel and to invert their positions.While satisfying the integrity,noise is suppressed as much as possible.In this method,the kernel matrix dimension is artificially added by using kernel principal component analysis to reduce the steering vector deviation of the system.The noise in-tensity ratio of the remaining components is added as the constraint condition of the algorithm to reduce the possibility of misjudg-ment of noise.Experiments results show that this method is superior to traditional TomoSAR inversion in all aspects,and the height reconstruction accuracy is improved to a certain extent.
万方数据
维普
