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 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 Polarimetric Target Decomposition (PTD). In our work, by constructing the polarimetric conceptual labels and a novel structure named Parallel Concept Bottleneck Networks (PaCBM), the uninterpretable high-dimensional features are transformed into human-comprehensible concepts based on physically verifiable polarimetric scattering mechanisms. Furthermore, we replace the standard Multi-Layer Perceptron (MLP) with a Kolmogorov–Arnold Network (KAN) to provide a more concise and transparent mapping with enhanced non-linear 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.