ABSTRACT Polarimetric Synthetic Aperture Radar (PolSAR) is one of the most important remote sensing tools. However, PolSAR images are strongly contaminated by a multidimensional interference (called speckle noise), making their processing (e.g. in the classification context) difficult. In terms of structure, multilook PolSAR data follow a definite positive hermitian behaviour and, therefore, require tailored classifiers for their features. Some classic classifiers – such as Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), K-Nearest Neighbours (KNN), and Support Vector Machine (SVM) – have been yielding unacceptable performance to these data, when applied directly. One justification is because they do not often take into account neither the speckle presence nor properties which are inherent to under-study relieves. This paper addresses a collection of PolSAR divergence-based classifiers, deduced from the normal, skew-normal, t-Student, and skew-t vector models as well as the scaled complex Wishart (SCW) distribution. The last model is a standard supposition to describe multilook PolSAR data, having two parameters: covariance matrix (which is directed to data nature) and number of looks (which controls the speckle noise effect). The considered remainder laws aim to model the main diagonal of these data, known as multivariate intensities. The performance of proposed methods is quantified and compared with those due to the Kullback-Leibler (KL) distance for multivariate normal distribution and to LDA, QDA, KNN, and SVM methods. Experiments with both artificial and real PolSAR data are considered. Results favour optimal Rényi classifiers for an Airborne Synthetic Aperture Radar (AIRSAR) image of San Francisco and the t-Student KL classifier for an SAR image system of the Electromagnetics Institute (EMISAR) image of Foulum.

中文翻译：

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用于极化合成孔径雷达 (PolSAR) 图像矢量和矩阵特征的一系列基于发散的分类器

摘要 极化合成孔径雷达（PolSAR）是最重要的遥感工具之一。然而，PolSAR 图像受到多维干扰（称为斑点噪声）的严重污染，使得它们的处理（例如在分类上下文中）变得困难。在结构方面，多视 PolSAR 数据遵循明确的正厄米特行为，因此需要针对其特征定制分类器。一些经典的分类器，例如线性判别分析 (LDA)、二次判别分析 (QDA)、K-最近邻 (KNN) 和支持向量机 (SVM)，在直接应用时对这些数据产生了不可接受的性能。一个理由是因为他们通常既不考虑斑点的存在，也不考虑研究不足所固有的特性。本文讨论了一系列基于 PolSAR 散度的分类器，这些分类器是从正态、偏正态、t-Student 和 skew-t 向量模型以及缩放的复数 Wishart (SCW) 分布推导出来的。最后一个模型是描述多视 PolSAR 数据的标准假设，具有两个参数：协方差矩阵（针对数据性质）和观察次数（控制散斑噪声效应）。所考虑的余数定律旨在对这些数据的主对角线进行建模，称为多变量强度。所提出方法的性能被量化并与由于多元正态分布的 Kullback-Leibler (KL) 距离以及 LDA、QDA、KNN 和 SVM 方法而产生的性能进行比较。考虑了人工和真实 PolSAR 数据的实验。