Integrated spatial-contextual remote sensing classification via dual-path transformers and entropy-regularized HSIC

Introduction Remote sensing image classification is an important task in Earth observation. However, achieving high accuracy is still challenging. This is mainly due to high-dimensional feature redundancy, large intra-class variability, and the difficulty of capturing both fine spatial details and long-range contextual information. To address these challenges, this paper proposes a unified classification framework based on a novel Multi-Scale Dual-Path Shifted Pyramid Vision Transformer (M-DSPViT). Methods The proposed model improves standard Vision Transformer architectures by introducing dual-path shifted patch embedding and content-adaptive attention gating. It also incorporates multi-scale feature pyramid fusion, dynamic expert routing, and gradient-based attention masking to better capture spatial and contextual features. In addition, a hybrid feature selection method (HSIC-HFS) is introduced to remove redundant information and retain discriminative features. This method combines the Hilbert-Schmidt independence criterion, Shannon entropy, and L 1 -regularization. The refined features are then integrated with CNN-based spatial descriptors extracted using EfficientNet through a Sequential Feature Aggregation (SFA) framework. Results The proposed method is evaluated on the WHU-RS19, UC Merced, and AID benchmark datasets. It achieves state-of-the-art performance in land-use classification. The robustness and generalization ability of the model are further validated through statistical analysis, including one-way ANOVA and F-statistic testing. Discussion The results confirm the stability of the proposed approach across different remote sensing scenarios.