The Wavelet Estimator Of Projection Indies And Application

Nowadays, traditional methods of image segmentation have been challenged by the multiply noise and non-nomal data of SAR image. Projection pursuit is an effective method for dimension reduced. The estimation of projection index is the core of projection pursuit method. Based on the study of the wavelet estimator of projection index, we explore the segmentation of SAR image used multi-scale projection pursuit.In this paper, we utilized the wavelet to estimate Cook's, Friedman's and Hall's projection index, and proved that the wavelet estimator of this projection pursuit indices are MSE convergence and asymptotically unbiased., and the wavelet estimator of the PPDA index is asymptotically unbiased.Estimating the projection index required to estimate the density function of projection data. In this paper, we compared the density function of kernel estimator and the density function of the wavelet estimator through simulation. The result indicated that the wavelet estimator better than the kernel estimator in some conditions.At last, we proposed a new character extraction method, so called Multi-scale Projection character. As well we give a way to segment SAR image by Multi-scale projection pursuit. From the segmentation results of the single-band and the Multi-band SAR image, we can see that this segmentation method is effective.