Hybrid WideResNet-Dual Spatial Embedding Vision Transformer with SE blocks: a high-accuracy model for Land Use and Land Cover classification in remote sensing

Land Use and Land Cover (LULC) classification is critical for environmental monitoring and sustainable resource management, but faces challenges in accurately capturing complex spatial-spectral features and long-range dependencies in remote sensing imagery. To address this, we introduce a Hybrid Wide Residual Network-Dual Spatial Positional Embedding Vision Transformer (WRN-DSPViT) framework enhanced with Squeeze-and-Excitation (SE) blocks and dual spatial positional embeddings. This model integrates a Wide Residual Network (WRN) for local spatial feature extraction and a Vision Transformer (ViT) with novel dual spatial encoding to capture global context via multi-head self-attention, where SE blocks dynamically recalibrate channel-wise features. Attention pooling is employed to fuse spatial features, allowing for adaptive weighting of important regions in the image, further enhancing classification accuracy. For multimodal hyperspectral-LiDAR data (Houston 2013), we extend this framework with parallel WRN-SE streams and cross-modal transformers, preserving spatial relationships through dual encodings. Evaluated on three benchmarks—EuroSAT (Multispectral), Houston 2013 (hyperspectral-LiDAR), and DeepGlobe (road extraction) —the hybrid WRN-DSPViT achieves state-of-the-art performance: 98.80% accuracy on EuroSAT (3.26 M parameters, 201.68 MFLOPS), 91.24% overall accuracy and 92.18% average accuracy on Houston 2013, and 0.802 F1-score/0.660 IoU on DeepGlobe.