多源卫星遥感影像时空融合研究的现状及展望

黄波; 赵涌泉

doi:10.11947/j.AGCS.2017.20170376

测绘学报 >

2017 , Vol. 46 >Issue 10: 1492 - 1499

DOI: https://doi.org/10.11947/j.AGCS.2017.20170376

摄影测量学与遥感

多源卫星遥感影像时空融合研究的现状及展望

黄波 ,
赵涌泉

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1. 香港中文大学地理与资源管理学系, 香港;
2. 香港中文大学太空与地球信息科学研究所, 香港;
3. 香港中文大学深圳研究院, 深圳 518057

黄波(1968-),男,博士,长江学者讲座教授,研究方向为遥感图像融合、时空大数据分析、可持续城市空间规划等。E-mail:bohuang@cuhk.edu.hk

收稿日期: 2017-07-03

修回日期: 2017-09-14

网络出版日期: 2017-10-26

基金资助

国家自然科学基金（41371417）

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Research Status and Prospect of Spatiotemporal Fusion of Multi-source Satellite Remote Sensing Imagery

HUANG Bo ,
ZHAO Yongquan

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1. Department of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong, China;
2. Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Hong Kong, China;
3. Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen 518057, China

Received date: 2017-07-03

Revised date: 2017-09-14

Online published: 2017-10-26

Supported by

The National Natural Science Foundation of China (No. 41371417)

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摘要

高空间分辨率的地表或者大气环境动态监测需要高时间-空间分辨率的卫星遥感影像作为数据支撑，但由于卫星传感器硬件技术及卫星发射成本等客观因素的限制，使得获取高时空分辨率遥感影像的较为便捷高效、低成本的可行手段就是将分别具有高时间和高空间分辨率的多源遥感影像进行时空融合，从而生成不同研究和应用所需的高时空分辨率卫星影像。现阶段，虽然国内外的学者进行了大量的时空融合算法研究，但是这些研究都局限于特定的数据类型、算法原理、应用目的等客观限制，而且其发展呈现出多样性。本文对现有主流的时空融合算法研究进行了归纳总结，将其分为4种：①基于地物组分的时空融合；②基于地表空间信息的时空融合；③基于地物时相变化的时空融合；④组合性的时空融合。同时，本文还对时空融合算法中存在的问题和面临的挑战进行了分析，并对其未来的发展方向进行了前瞻性的展望。

关键词： 多源遥感影像; 时空分辨率折中; 时空融合; 地物组分; 空间信息; 时相变化

本文引用格式

黄波 , 赵涌泉 . 多源卫星遥感影像时空融合研究的现状及展望[J]. 测绘学报, 2017 , 46(10) : 1492 -1499 . DOI: 10.11947/j.AGCS.2017.20170376

Abstract

High spatial resolution monitoring of land surface and atmospheric environment dynamics requires high spatiotemporal resolution satellite remote sensing imagery as data support. However, the efficient, low-cost, and feasible solution is to blend the multi-source images with high-spatial and high-temporal resolution respectively to produce the desired high spatiotemporal resolution imagery required by different research or applications, which is subject to the limitations of satellite sensor' hardware technology and the budget constraints of launching more satellites. Although plenty of spatiotemporal image fusion research has been conducted, they are limited to specific data types, algorithm principles, application purposes, etc. Furthermore, the development of spatiotemporal image fusion algorithm presents a phenomenon of disorder. This study summarizes and generalizes the existing mainstream spatiotemporal fusion methods and classified them into four categories:①spatiotemporal fusion based on land components; ②spatiotemporal fusion based on spatial information; ③spatiotemporal fusion based on temporal changes; ④combined spatiotemporal fusion. Meanwhile, the study analyzes the problems and challenges faced by spatiotemporal fusion; and informs the prospects of the future development of spatiotemporal fusion method.

Key words： multi-source remote sensing imagery; spatiotemporal resolution compromise; spatiotemporal fusion; land component; spatial information; temporal change

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