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为了能够同时增强多类目标,提出一种基于Poisson重建的极化合成孔径雷达(SAR)图像对比增强方法。该方法对多类目标和对应的背景杂波分别进行广义相对最优极化增强(GOPCE),并得到相应的最优极化状态和特征系数;以此定义图像中各像素点的最优局部梯度,并在最小二乘准则下,根据局部梯度建立离散Poisson方程;通过快速Fourier变换求解该Poisson方程,得到最终的多目标增强图像。实验结果表明:利用极化SAR数据,使用该方法增强后的图像的直方图保持应有的峰值,且更加均衡,能够达到增强多类目标的效果,从而有利于目标检测等后续处理。
In order to enhance multiple targets at the same time, a Poisson-Reconstructed Polarimetric Synthetic Aperture Radar (SAR) image contrast enhancement method is proposed. This method performs generalized relative optimal polarization enhancement (GOPCE) for multiple classes of targets and corresponding background clutter, respectively, and obtains the corresponding optimal polarization states and eigencoefficients. In this way, the optimal local part of each pixel in the image is defined In the least squares criterion, a discrete Poisson equation is established according to the local gradient. The Poisson equation is solved by fast Fourier transform to obtain the final multi-target enhanced image. The experimental results show that using the polarized SAR data, the histogram of the enhanced image using this method can maintain the proper peak value and be more balanced, which can enhance the effect of many kinds of targets so as to be beneficial to the subsequent detection of the target detection.