ABSTRACT

A random patches-based edge-preserving network (RPEP) is proposed for polarimetric synthetic aperture radar (PolSAR) image classification in this paper. An initial spatial feature extraction is firstly done using the transform domain recursive filtering. From the filtered images, several random patches are chosen and used as convolutional kernels. The designed random patches-based network uses these fix kernels without doing any training process. The multi-scale features extracted by both shallow and deep layers are given to a support vector machine to get an initial classification map. The binary probability maps obtained from the initial classification map are then smoothed using the guided filter as an edge preserving filter. The final classification map is achieved by applying the maximum decision rule. The proposed RPEP method with extraction of robust, consistent, invariant and multi-scale polarimetric-spatial features and also by doing noise reduction with edge preserving filters provides superior classification results compared to several state-of-the-art methods especially in small sample size situations. In addition, RPEP has a simple and fast implementation that makes it a powerful classifier for real applications.

摘要

针对极化合成孔径雷达（PolSAR）图像分类问题，提出了一种基于随机补丁的边缘保持网络（RPEP）。首先使用变换域递归滤波完成初始空间特征提取。从滤波后的图像中，选择了几个随机块并将其用作卷积核。设计的基于随机补丁的网络无需进行任何培训即可使用这些修复内核。将浅层和深层提取的多尺度特征提供给支持向量机，以获得初始分类图。然后，使用引导滤波器作为边缘保留滤波器，对从初始分类图获得的二元概率图进行平滑处理。最终分类图是通过应用最大决策规则来实现的。所提出的RPEP方法具有可靠，一致，不变和多尺度极化空间特征的提取功能，并且通过使用边缘保留滤波器进行降噪处理，与几种最新方法相比，尤其是在小样本量的情况下，可提供出色的分类结果情况。此外，RPEP具有简单快速的实现方式，使其成为用于实际应用程序的强大分类器。