The typical processing chain for pixel-wise classification from PolSAR images starts with an optional preprocessing step (e.g. speckle reduction), continues with extracting features projecting the complex-valued data into the real domain (e.g. by polarimetric decompositions) which are then used as input for a machine-learning based classifier, and ends in an optional postprocessing (e.g. label smoothing). The extracted features are usually hand-crafted as well as preselected and represent (a somewhat arbitrary) projection from the complex to the real domain in order to fit the requirements of standard machine-learning approaches such as Support Vector Machines or Artificial Neural Networks. This paper proposes to adapt the internal node tests of Random Forests to work directly on the complex-valued PolSAR data, which makes any explicit feature extraction obsolete. This approach leads to a classification framework with a significantly decreased computation time and memory footprint since no image features have to be computed and stored beforehand. The experimental results on one fully-polarimetric and one dual-polarimetric dataset show that, despite the simpler approach, accuracy can be maintained (decreased by only less than $2\%$ for the fully-polarimetric dataset) or even improved (increased by roughly $9\%$ for the dual-polarimetric dataset).

用于从PolSAR图像按像素进行分类的典型处理链始于可选的预处理步骤（例如斑点减少），接着是提取将复杂值数据投影到真实域中的特征（例如通过极化分解），然后将其用作输入用于基于机器学习的分类器，并以可选的后处理（例如，标签平滑）结束。提取的特征通常是手工制作的，也是预先选择的，代表从复杂到真实域的投影（某种程度任意），以适应标准机器学习方法（如支持向量机或人工神经网络）的要求。本文提出将随机森林的内部节点测试改编为直接用于复值PolSAR数据，这使得任何显式的特征提取都变得过时了。这种方法导致分类框架的计算时间和内存占用显着减少，因为无需事先计算和存储图像特征。在一个全极化数据集和一个双极化数据集上的实验结果表明，尽管采用了更简单的方法，但仍可以保持精度（降低幅度仅小于$\mathrm{2个}％$ 对于全极化数据集）甚至有所改善（大致增加了 $9％$ （用于双极化数据集）。