ABSTRACT

The data classification of fully polarimetric synthetic aperture radar (PolSAR) is one of the favourite topics in the remote sensing community. To date, a wide variety of algorithms have been utilized for PolSAR data classification, and among them kernel methods are the most attractive algorithms for this purpose. The most famous kernel method, i.e., the support vector machine (SVM) has been widely used for PolSAR data classification. However, until now, no studies to classify PolSAR data have been carried out using certain extended SVM versions, such as the least squares support vector machine (LSSVM), relevance vector machine (RVM) and import vector machine (IVM). Therefore, this work has employed and compared these four kernel methods for the classification of three PolSAR data sets. These methods were compared in two groups: the SVM and LSSVM as non-probabilistic kernel methods vs. the RVM and IVM as probabilistic kernel methods. In general, the results demonstrated that the SVM was marginally better, more accurate and more stable than the probabilistic kernels. Furthermore, the LSSVM performed much faster than the probabilistic kernel methods and its associated version, the SVM, with comparable accuracy.

摘要

全极化合成孔径雷达（PolSAR）的数据分类是遥感界最喜欢的话题之一。迄今为止，已使用多种算法进行 PolSAR 数据分类，其中核方法是用于此目的的最有吸引力的算法。最著名的核方法，即支持向量机(SVM) 已广泛用于PolSAR 数据分类。然而，到目前为止，还没有使用某些扩展 SVM 版本对 PolSAR 数据进行分类的研究，例如最小二乘支持向量机 (LSSVM)、相关向量机 (RVM) 和输入向量机 (IVM)。因此，这项工作采用并比较了这四种核方法对三个 PolSAR 数据集的分类。这些方法在两组中进行了比较：SVM 和 LSSVM 作为非概率核方法与 RVM 和 IVM 作为概率核方法。总的来说，结果表明 SVM 比概率内核更好、更准确和更稳定。此外，LSSVM 的执行速度比概率核方法及其相关版本 SVM 快得多，精度相当。