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Integrated spatial-contextual remote sensing classification via dual-path transformers and entropy-regularized HSIC
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DOI：10.3389/frsen.2026.1834812.png)
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Abstract
En 中文
IntroductionRemote 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).MethodsThe 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 L1-regularization. The refined features are then integrated with CNN-based spatial descriptors extracted using EfficientNet through a Sequential Feature Aggregation (SFA) framework.ResultsThe 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.DiscussionThe results confirm the stability of the proposed approach across different remote sensing scenarios.
Keywords:
remote sensing
vision transformer
dual-path attention
land-use classification
sequential feature aggregation
spatial-contextual feature learning
Journal
F
IF:
3.7
Papers:
569
Citations:
993
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