Acta Geodaetica et Cartographica Sinica ›› 2022, Vol. 51 ›› Issue (7): 1317-1337.doi: 10.11947/j.AGCS.2022.20220171

• Photogrammetry and Remote Sensing • Previous Articles     Next Articles

Data-driven multi-source remote sensing data fusion: progress and challenges

ZHANG Liangpei1, HE Jiang2, YANG Qianqian2, XIAO Yi2, YUAN Qiangqiang2   

  1. 1. State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China;
    2. School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China
  • Received:2022-02-28 Revised:2022-07-11 Published:2022-08-13
  • Supported by:
    The National Natural Science Foundation of China (Nos. 41922008|61971319)

Abstract: Multi-source remote sensing data fusion is an important technology to generate seamless observation data of large scene with a high temporal-spatial-spectral resolution, which breaks through the limitation of single sensor observation and realize the complementary utilization of multi-platform and multi-mode observation data. With the improvement of artificial intelligence theory and technology, data-driven multi-source remote sensing data fusion has been widely favored by researchers. However, the inherent low physical interpretability and weak generalization ability of data-driven algorithms have impeded its further development in multi-source remote sensing data fusion. Therefore, this paper systematically summarizes the researches of homogeneous remote sensing data fusion, heterogeneous remote sensing data fusion and point-surface fusion through three sections, and analyzes the trend of each fusion problem. Finally, this paper discusses the challenges faced by data-driven fusion algorithm, and points out some feasible future directions of multi-source remote sensing data fusion, which provides some suggestions for researchers in this field.

Key words: remote sensing, multi-source fusion, information fusion, data-driven, model-driven, deep learning

CLC Number: