We present an in-depth statistical comparison among several Complex-Valued Neural Network (CVNN) models on the Oberpfaffenhofen Polarimetric Synthetic Aperture Radar (PolSAR) database and compare them against Real-Valued Neural Network (RVNN) architectures. The necessity to define the equivalence between the models emerges in order to compare both networks fairly. A novel definition for an equivalent-RVNN in terms of real-valued trainable parameters that maintain the aspect ratio is extended for convolutional layers based on previous work Barrachina et al. (2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP), 2021). We illustrate that CVNN obtains better statistical performance for classification on the PolSAR image across a range of architectures than a capacity equivalent-RVNN, indicating that this behavior is likely independent of the model itself.

我们在 Oberpfaffenhofen 极化合成孔径雷达 (PolSAR) 数据库上对几个复值神经网络 (CVNN) 模型进行了深入的统计比较，并将它们与实值神经网络 (RVNN) 架构进行了比较。为了公平地比较两个网络，出现了定义模型之间等价性的必要性。根据保持纵横比的实值可训练参数对等价 RVNN 的新定义基于之前的工作 Barrachina 等人，对卷积层进行了扩展。（2021 年 IEEE 第 31 届信号处理机器学习国际研讨会 (MLSP)，2021 年）。我们说明 CVNN 在一系列架构中对 PolSAR 图像的分类获得了比容量等效的 RVNN 更好的统计性能，这表明这种行为可能与模型本身无关。