| 数据驱动的多源遥感信息融合研究进展 |
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| 引用本文： | 张良培,何江,杨倩倩,肖屹,袁强强.数据驱动的多源遥感信息融合研究进展[J].测绘学报,2022,51(7):1317-1337. |
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| 作者姓名： | 张良培 何江 杨倩倩 肖屹 袁强强 |
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| 作者单位： | 1. 武汉大学测绘遥感信息工程国家重点实验室, 湖北 武汉 430079;2. 武汉大学测绘学院, 湖北 武汉 430079 |
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| 基金项目： | 国家自然科学基金(41922008;61971319) |
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| 摘 要： | 多源遥感信息融合技术是突破单一传感器的观测局限,实现多平台多模态观测信息互补利用,生成大场景高“时-空-谱”无缝的观测数据的重要手段。随着人工智能理论与技术的日益完善,数据驱动的多源遥感信息融合获得了研究者的广泛青睐,然而,数据驱动算法与生俱来的低物理可解释性,弱泛化能力都阻碍了其在多源遥感信息融合领域的长远发展。因此,本文分别对同质遥感数据融合,异质遥感数据融合,以及点-面融合的有关研究成果进行了系统的梳理和归纳,分析了各融合问题的发展趋势。最后,对算法研究进展进行了总结,剖析了数据驱动的融合算法所面临的挑战,指出了未来多源遥感信息融合领域的研究方向。
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| 关 键 词： | 遥感 多源融合 信息融合 数据驱动 模型驱动 深度学习 |
| 收稿时间： | 2022-02-28 |
| 修稿时间： | 2022-07-11 |
Data-driven multi-source remote sensing data fusion: progress and challenges |
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| ZHANG Liangpei,HE Jiang,YANG Qianqian,XIAO Yi,YUAN Qiangqiang.Data-driven multi-source remote sensing data fusion: progress and challenges[J].Acta Geodaetica et Cartographica Sinica,2022,51(7):1317-1337. |
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| Authors: | ZHANG Liangpei HE Jiang YANG Qianqian XIAO Yi YUAN Qiangqiang |
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| Institution: | 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 |
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| 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. |
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