测绘学报 ›› 2017, Vol. 46 ›› Issue (10): 1447-1459.doi: 10.11947/j.AGCS.2017.20170340

• 摄影测量学与遥感 • 上一篇    下一篇

多时相遥感影像变化检测的现状与展望

张良培1, 武辰2   

  1. 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. 武汉大学国际软件学院, 湖北 武汉 430079
  • 收稿日期:2017-06-21 修回日期:2017-08-03 出版日期:2017-10-20 发布日期:2017-10-26
  • 作者简介:张良培(1962-),男,博士,教授,研究方向为遥感影像处理、分析与应用。E-mail:zlp62@whu.edu.cn
  • 基金资助:
    国家自然科学基金(61601333;41431175);湖北省自然科学基金(2016CFB245);中央高校基本科研业务费专项基金(2042016kf0034)

Advance and Future Development of Change Detection for Multi-temporal Remote Sensing Imagery

ZHANG Liangpei1, WU Chen2   

  1. 1. State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China;
    2. International School of Software, Wuhan University, Wuhan 430079, China
  • Received:2017-06-21 Revised:2017-08-03 Online:2017-10-20 Published:2017-10-26
  • Supported by:
    The National Natural Science Foundation of China(Nos. 61601333;41431175);The Natural Science Foundation of Hubei Province of China(No. 2016CFB245);The Fundamental Research Funds for the Central Universities(No. 2042016kf0034)

摘要: 多时相遥感影像变化检测技术能够监测生态环境变化、跟踪城市发展,对于研究人类与自然环境之间的交互关系有着重要的意义。随着新型遥感影像的不断普及,变化检测也在高光谱影像变化检测和高分辨率影像变化检测两个方向上有了深入的探索。本文围绕变化检测的基本流程,从预处理、变化检测方法、阈值分割与精度评价4个角度介绍了变化检测研究的最新进展,并总结了变化检测技术的主要应用领域。最后,对变化检测技术未来的发展进行了展望。

关键词: 变化检测, 遥感影像, 多时相, 高分辨率, 高光谱

Abstract: Change detection in multi-temporal remote sensing images can be widely applied in monitoring ecosystem changes,and tracking urban developments,thus is extremely useful to study the interaction between human and natural environment.With the development of new remote sensing technology,change detection has attracted more and more interests in dealing with multi-temporal hyperspectral and high-resolution remote sensing images.In this review,the recent advances are introduced in four aspects:pre-processing,change detection method,thresholding and accuracy assessment.Then,the main applications for change detection are summarized.And finally,the future developments of change detection are discussed.

Key words: change detection, remote sensing imagery, multi-temporal data, high-resolution image, hyperspectral image

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