CSTFNet: A CNN and Dual Swin-Transformer Fusion Network for Remote Sensing Hyperspectral Data Fusion and Classification of Coastal Areas

Abstract

Hyperspectral imaging (HSI) can capture a large amount of spectral information at various wavelengths, enabling detailed material classification and identification, making it a key tool in remote sensing, particularly for coastal area monitoring. In recent years, the convolutional neural network (CNN) framework and transformer models have demonstrated strong performance in HSI classification, especially in applications requiring precise change detection and analysis. However, due to the high dimensionality of HSI data and the complexity of spectral-spatial feature extraction, achieving accurate results in coastal areas remains challenging. This article introduces a new hybrid model, CSTFNet, which combines an improved CNN module and dual-layer Swin transformer (DLST) to tackle these challenges. CSTFNet integrates spectral and spatial processing capabilities, significantly reducing computational complexity while maintaining high classification accuracy. The improved CNN module employs one-dimensional convolutions to handle high-dimensional data, while the DLST module uses window-based multihead attention to capture both local and global dependencies. Experiments conducted on four standard HSI datasets (Houston-2013, Samson, KSC, and Botswana) demonstrate that CSTFNet outperforms traditional and state-of-the-art algorithms, achieving overall classification accuracy exceeding 99% . In particular, on the Houston-2013 dataset, the results for OA and AA are 1.00 and the kappa coefficient is 0. 976. The results highlight the robustness and efficiency of the proposed model in coastal area applications, where accurate and reliable spectral-spatial classification is crucial for monitoring and environmental management.