The model-based polarimetric decomposition is extensively studied due to its simplicity and clear physical interpretation of Polarimetric Synthetic Aperture Radar (PolSAR) data. Though there are many fine basic scattering models and well-designed decomposition methods, the overestimation of volume scattering (OVS) may still occur in highly oriented buildings, resulting in severe scattering mechanism ambiguity. It is well known that not only vegetation areas but also oriented buildings may cause intense cross-pol power. To improve the scattering mechanism ambiguity, an appropriate scattering model for oriented buildings and a feasible strategy to assign the cross-pol power between vegetation and oriented buildings are of equal importance. From this point of view, we propose a five-component decomposition method with a general rotated dihedral scattering model and an assignment strategy of cross-pol power. The general rotated dihedral scattering model is established to characterize the integral and internal cross-pol scattering from oriented buildings, while the assignment of cross-pol power between volume and rotated dihedral scattering is achieved by using an eigenvalue-based descriptor ${\mathrm{D}}_{\mathrm{OOB}}$. In addition, a simple branch condition with explicit physical meaning is proposed for model parameters inversion. Experiments on spaceborne Radarsat−2 C band and airborne UAVSAR L band PolSAR datasets demonstrate the effectiveness and advantages of the proposed method in the quantitative characterization of scattering mechanisms, especially for highly oriented buildings.

Few-Shot Class-Incremental Learning (FSCIL) aims to continually learn novel classes from limited data while retaining knowledge of previously learned classes. To mitigate catastrophic forgetting, most approaches pre-train a powerful backbone on the base session and keep it frozen during incremental sessions. Within this framework, existing studies primarily focus on representation learning in FSCIL, particularly Self-Supervised Contrastive Learning (SSCL), to enhance the transferability of representations and thereby boost model generalization to novel classes. However, they face a trade-off dilemma: improving transferability comes at the expense of discriminability, precluding simultaneous high performance on both base and novel classes. To address this issue, we propose BR-FSCIL, a representation learning framework for the FSCIL scenario. In the pre-training stage, we first design a Hierarchical Contrastive Learning (HierCon) algorithm. HierCon leverages label information to model hierarchical relationships among features. In contrast to SSCL, it maintains strong discriminability when promoting transferability. Second, to further improve the model’s performance on novel classes, an Alignment Modulation (AM) loss is proposed that explicitly facilitates learning of knowledge shared across classes from an inter-class perspective. Building upon the hierarchical discriminative structure established by HierCon, it additionally improves the model’s adaptability to novel classes. Through optimization at both intra-class and inter-class levels, the representations learned by BR-FSCIL achieve a balance between discriminability and transferability. Extensive experiments on mini-ImageNet, CIFAR100, and CUB200 demonstrate the effectiveness of our method, which achieves final session accuracies of 53.83%, 53.04%, and 62.60%, respectively.