WSC-Net: A Wavelet-Enhanced Swin Transformer with Cross-Domain Attention for Hyperspectral Image Classification

Abstract

Highlights: What are the main findings? A novel dual-branch architecture, WSC-Net, was developed to synergistically integrate a Swin Transformer backbone with a parallel Wavelet Transform Module via a new Cross-Domain Attention Fusion (CDAF) mechanism. The proposed WSC-Net consistently outperforms state-of-the-art hyperspectral image classification methods, demonstrating a superior ability to preserve fine-grained local details without sacrificing global contextual understanding. What are the implications of the main findings? This study demonstrates that the intelligent fusion of spatial-domain and frequency-domain features is a highly effective strategy to overcome the inherent performance trade-offs of single-paradigm deep learning models in HSI analysis. The proposed Cross-Domain Attention Fusion (CDAF) module provides a flexible and powerful blueprint for integrating features from disparate domains, offering a promising pathway for developing more robust multi-modal and multi-scale models in remote sensing. This paper introduces the Wavelet-Enhanced Swin Transformer Network (WSC-Net), a novel dual-branch architecture that resolves the inherent tradeoff between global spatial contextual and fine-grained spectral details in hyperspectral image (HSI) classification. While transformer-based models excel at capturing long-range dependencies, their patch-based nature often overlooks intra-patch high-frequency details, hindering the discrimination of spectrally similar classes. Our framework synergistically couples a two-stage Swin transformer with a parallel Wavelet Transform Module (WTM) for local frequency information capture. To address the semantic gap between spatial and frequency domains, we propose the Cross-Domain Attention Fusion (CDAF) module—a bi-directional attention mechanism that facilitates intelligent feature exchange between the two streams. CDAF explicitly models cross-domain dependencies, amplifies complementary features, and suppresses noise through attention-guided integration. Extensive experiments on four benchmark datasets demonstrate that WSC-Net consistently outperforms state-of-the-art methods, confirming its effectiveness in balancing global contextual modeling with local detail preservation.