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.