Toward Interpretable PolSAR Image Classification: Polarimetric Scattering Mechanism Informed Concept Bottleneck and Kolmogorov–Arnold Network

In recent years, deep learning (DL)-based methods have received extensive and sufficient attention in the field of polarimetric synthetic aperture radar (PolSAR) image classification, which show excellent performance. However, due to the “black-box” nature of DL methods, the interpretation of the high-dimensional features extracted and the backtracking of the decision-making process based on the features are still unresolved problems. In this study, we first highlight this issue and attempt to achieve the interpretability analysis of DL-based PolSAR image classification technology with the help of the polarimetric target decomposition (PTD). In our work, by constructing the polarimetric conceptual labels and a novel structure named parallel concept bottleneck models (PaCBM), the uninterpretable high-dimensional features are transformed into human-comprehensible concepts based on physically verifiable polarimetric scattering mechanisms. Furthermore, we replace the standard multilayer perceptron (MLP) with a Kolmogorov–Arnold network (KAN) to provide a more concise and transparent mapping with enhanced nonlinear modeling. Experiments on multiple PolSAR datasets demonstrate that our approach maintains high-classification accuracy while yielding explicit analytical functions linking conceptual and category labels, advancing interpretability in DL-based PolSAR image analysis.