Complex-Valued Neural-Network Inverse Mapping for Explainability in PolSAR/InSAR Applications

Gunjan Joshi, Ryo Natsuaki, Akira Hirose

Abstract: In recent years, the applications of complex-valued neural networks (CVNNs) have gained prominence in radar-related domains, notably in synthetic aperture radar and ground-penetrating radar. Unlike traditional real-valued neural networks, CVNNs encompass both amplitude and phase components, for inputs, weights, bias, and activations. In this paper, we adopt the inverse mapping architecture for neural network explainability to conduct feature importance analysis for enhancing the interpretability and transparency of CVNN’s decision. We propose this method for first identifying built-up areas, and then determining the prominent input features by inverse mapping. The feedforward CVNN achieved high training and validation accuracies of 97.09% and 99.25%, respectively. Additionally, the inverse mapping results identify the most significant contributing input features.