Swish-Cardioid CV-CNN: A Novel Complex-Valued Activation Framework for PolSAR Terrain Classification

Activation functions are pivotal in complex-valued convolutional neural networks (CV-CNNs) as they govern the nonlinear learning of amplitude–phase relationships in polarimetric synthetic aperture radar (PolSAR) data. This letter proposes a Swish-Cardioid activation function (SCAF), which introduces an adaptive gating mechanism inspired by Swish’s adaptive properties into the Cardioid activation framework. SCAF multiplies the activation value by the output of a gating function, enabling automatic adjustment of nonlinear intensity and output range based on input characteristics. This innovation enhances the model’s flexibility and adaptability in handling complex electromagnetic scattering patterns. Experimental results demonstrate that the proposed method achieves superior performance in terrain classification tasks.