本文提出了一种结合相干斑抑制的全极化 SAR(Synthetic Aperture Radar)图像分类新方法.该方法先对图像数据做Pauli分解,获得三个极化组合通道,并分别用三种颜色表示这三个极化组合;再用独立分量分析稀疏编码(ICA-SCS)算法对各颜色通道进行相干斑抑制,最后把三个颜色通道混合,实现了对图像信息的分类.该方法很好的保留了极化通道间的相对相位信息,同时,相干斑抑制后的数据直接用于图像分类,不需要再做任何极化通道组合.对真实SAR图像的分类结果表明,该方法对分类效果和精度有明显改善.

A new method for full-polarization SAR images classification integrated with speckle reduction is proposed.SAR image is firstly decomposed by Pauli-decomposition approach and three combined polarization channels which are denoted by three different colors are obtained.Then the speckle of each color channel is reduced by using ICA sparse-coding shrinkage algorithm (ICA-SCS).After speckle reduction,the three channels are constituted to rebuild a classified image with less speckle noise.This method well keeps the relative phase information among the polarization channels.Furthermore,the channels after speckle reduction are directly used for classification without any other combination.The classification results of real SAR images indicate that the classification accuracy is improved obviously.