A Complex-Valued Multiscale Feature Interaction Attention Vision Transformer for PolSAR Image Classification with Amplitude–Phase Decoupling

Limited labeled data and class imbalance complicate polarimetric synthetic aperture radar (PolSAR) image classification. We propose a complex-valued multiscale feature interaction attention vision transformer (CV-MfIaViT) that combines spatial analytic-envelope preprocessing, complex-valued local feature extraction and global modeling. A two-dimensional Hilbert transform (2D HT) provides envelope features to the shallow pathway. The complex-valued amplitude–phase interaction attention (CV-APIA) module generates positive real gains through cross-modulation of pooled features; the final gating preserves the element-wise phase of non-zero input features. A complex-valued feature complementary mapper (CV-FCM), a complex-valued multiscale extractor (CV-MSE) and a complex-valued vision transformer (CV-ViT) complete the architecture. Under random-pixel sampling, the reported average accuracy (AA) exceeds CV-MsAtViT by 1.83, 3.42 and 2.48 percentage points on Flevoland, San Francisco and Nanjing, respectively. The advantage is also retained under spatial-block partitioning with shared label-free scene-level normalization. Component ablations and comparisons with two simple learned front ends characterize the combined design. The results support the evaluated architecture without isolating phase preservation as the cause of its accuracy gains.