论文标题
用于语义分割波尔萨图像的随机蕨类植物
Random Ferns for Semantic Segmentation of PolSAR Images
论文作者
论文摘要
随机蕨类植物（作为集合学习的鲜为人知的例子）已成功应用于许多计算机视觉应用程序，从关键点匹配到对象检测。本文将随机的蕨类框架扩展到极化合成孔径雷达图像的语义分割。通过使用在Hermitian矩阵空间上定义的内部投影，可以将所提出的分类器直接应用于极化协方差矩阵，而无需明确计算预定义的图像特征。此外，提出了两种不同的优化策略：第一个基于在创建分类器之前的内部二进制特征的前选择和分组；第二个基于迭代改善给定随机蕨类植物的特性。两种策略都能够通过过滤冗余或具有低信息内容的特征以及对相关功能进行分组以最好地实现随机蕨类分类器做出的独立假设来提高性能。实验表明，可以实现与更复杂的随机森林模型相似的结果，并且与深度学习基线竞争。
Random Ferns -- as a less known example of Ensemble Learning -- have been successfully applied in many Computer Vision applications ranging from keypoint matching to object detection. This paper extends the Random Fern framework to the semantic segmentation of polarimetric synthetic aperture radar images. By using internal projections that are defined over the space of Hermitian matrices, the proposed classifier can be directly applied to the polarimetric covariance matrices without the need to explicitly compute predefined image features. Furthermore, two distinct optimization strategies are proposed: The first based on pre-selection and grouping of internal binary features before the creation of the classifier; and the second based on iteratively improving the properties of a given Random Fern. Both strategies are able to boost the performance by filtering features that are either redundant or have a low information content and by grouping correlated features to best fulfill the independence assumptions made by the Random Fern classifier. Experiments show that results can be achieved that are similar to a more complex Random Forest model and competitive to a deep learning baseline.
