[1]黄 波.时空遥感影像融合研究的进展与趋势[J].四川师范大学学报(自然科学版),2020,43(04):427-434.[doi:10.3969/j.issn.1001-8395.2020.04.001]
 HUANG Bo,.Research Progress and Trend of Spatial and Temporal Remote Sensing Image Fusion[J].Journal of SichuanNormal University,2020,43(04):427-434.[doi:10.3969/j.issn.1001-8395.2020.04.001]
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时空遥感影像融合研究的进展与趋势()
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《四川师范大学学报(自然科学版)》[ISSN:1001-8395/CN:51-1295/N]

卷:
43卷
期数:
2020年04期
页码:
427-434
栏目:
特约专稿
出版日期:
2020-06-20

文章信息/Info

Title:
Research Progress and Trend of Spatial and Temporal Remote Sensing Image Fusion
文章编号:
1001-8395(2020)04-0427-08
作者:
黄 波123
1. 香港中文大学 地理与资源管理学系, 香港 999079; 2. 香港中文大学 太空与地球信息科学研究所, 香港 999079; 3. 香港中文大学 深圳研究院, 广东 深圳 518057
Author(s):
HUANG Bo1 2 3
1. Department of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong 999079; 2. Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Hong Kong 999079; 3. Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen 518057, Guangdong)
关键词:
时空融合 遥感影像 不确定性 时间分辨率 空间分辨率
Keywords:
spatial and temporal fusion remote sensing images uncertainty temporal resolution spatial resolution
分类号:
P237
DOI:
10.3969/j.issn.1001-8395.2020.04.001
文献标志码:
A
摘要:
地表与大气环境的实时精细监测需要高时空分辨率的遥感影像提供数据支撑,然而,现有单一卫星传感器无法获取同时具有高空间与高时间分辨率的遥感影像.针对这一问题,国内外学者提出了大量的时空遥感影像融合算法,以低成本、便捷高效地生成满足不同应用需求的高时空分辨率遥感影像.总结现有主要的时空遥感影像融合算法并基于不同的算法原理将其分为4类:1)基于空间信息分解的融合方法,2)基于时空变化滤波的融合方法,3)基于学习的融合方法,4)组合性的融合方法.同时,讨论时空遥感影像融合的不确定性问题,并对其未来的发展趋势提出前瞻性的展望.
Abstract:
Real-time and fine monitoring of land surface and atmospheric environment requires remote sensing images with both high spatial and high temporal resolution as data support. However, data from a single satellite sensor are unable to satisfy our demand. Therefore, plenty of spatial and temporal image fusion algorithms have been proposed to produce the images with high spatiotemporal resolution. This paper summarizes the existing main fusion methods and classifies them into four categories: 1)spatial unmixing-based fusion method, 2)sptaio-temporal filter-based fusion method, 3)learning-based fusion method, and 4)hybrid fusion method. Besides, the uncertainties of the spatial and temporal remote sensing image fusion are discussed. Further, this paper prospects the future trend of the spatial and temporal image fusion.

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备注/Memo

备注/Memo:
收稿日期:2019-11-01 接受日期:2019-11-14
基金项目:国家自然科学基金(41371417)
作者简介:黄 波(1968—),男,教授,主要从事地理信息科学的研究,E-mail:bohuang@cuhk.edu.hk
更新日期/Last Update: 2020-06-20