太阳成8722(中国)有限公司-GREEN NO.1

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

作者: Dong, YN (Dong, Yanni); Yang, ZZ (Yang, Zhenzhen); Liu, QW (Liu, Quanwei); Zuo, RG (Zuo, Renguang); Wang, ZY (Wang, Ziye)

来源出版物: INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 卷: 129 文献号: 103780 DOI: 10.1016/j.jag.2024.103780 Early Access Date: MAR 2024 Published Date: 2024 MAY

摘要: 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 (ViTDGCN) 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.

作者关键词: Lithological mapping; Data fusion; Vision transformer; Graph convolutional network

地址: [Dong, Yanni] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

[Yang, Zhenzhen] China Univ Geosci, Sch Geophys & Geomat, Wuhan 430074, Peoples R China.

[Liu, Quanwei] James Cook Univ, Coll Sci & Engn, Cairns, Qld 4878, Australia.

[Zuo, Renguang; Wang, Ziye] China Univ Geosci, State Key Lab Geol Proc & Mineral Resources, Wuhan 430074, Peoples R China.

通讯作者地址: Wang, ZY (通讯作者)，China Univ Geosci, State Key Lab Geol Proc & Mineral Resources, Wuhan 430074, Peoples R China.

电子邮件地址: ziyewang@cug.edu.cn

影响因子：7.5