Fusion of GaoFen-5 and Sentinel-2B data for lithological mapping using vision transformer dynamic graph convolutional network

Abstract

Lithological identification and mapping using remote sensing (RS) imagery are challenging. Traditional lithological mapping relies mainly on multispectral data and machine learning methods. However, inadequate spectral information and inappropriate classification algorithms are major problems for RS geological applications. Moreover, satellite hyperspectral images (HSI) at low spatial resolution and convolutional neural network (CNN)-based methods with incomplete feature extraction remain challenging because of the limitations of sensor imaging and convolutional kernels for lithological mapping. To address the above issues, in this study, smoothing filter-based intensity modulation (SFIM) fusion technology is first employed to fuse GaoFen-5 hyperspectral images and Sentinel-2B multispectral images. This approach significantly improves spatial details and enriches spectral information. Subsequently, a novel Vision Transformer Dynamic Graph Convolutional Network (ViT-DGCN) is proposed for lithological mapping of the Cuonadong dome, Tibet, China. ViT-DGCN is a joint model consisting of a transformer and a dynamic graph convolution module that enhances feature extraction capabilities by exploring long-range interaction sequence features and dynamic graph structure information in a targeted manner. The proposed algorithm exhibits superior performance compared to the others, achieving an overall accuracy of 97% for the Cuonadong dome using only 1% of the available training samples.