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Received：23 April 2022，
Published：07 September 2023
移动端阅览
多时相遥感影像变化检测是指对同一地理区域、不同时间获取的遥感影像进行自动变化发现、识别与解释的遥感处理与分析技术。随着卫星遥感技术及人工智能理论方法的快速发展，基于多时相遥感影像数据驱动和模型驱动的传统变化检测方法正朝着数据—模型—知识联合驱动的方向转型和演变，以更加自动化、精细化和智能化的方式，解决多领域的地表时空变化检测问题。本文在总结多时相遥感数据源从同构到异构、变化检测模型从传统到智能、变化检测应用从理论到落地过程中存在问题的基础上，以光学遥感影像变化检测任务为例，梳理和分析了人工智能时代下变化检测技术的发展历程。从无监督、监督、弱监督3个方面探讨了遥感变化检测从传统到前沿技术的转型特点与趋势，并进一步提出了未来需重点突破模型的物理可解释性、泛化及迁移能力、跨数据—跨场景—跨领域应用水平等关键问题。
In the past decades
the effects of global climate change and the increase of human activities have remarkably increased the demand for remote sensing monitoring. Moreover
with the accumulation of remote sensing data from multiple platforms and multiple sensors
the quantity and quality of multitemporal images have substantially improved. Multitemporal remote sensing images Change Detection (CD) is a processing and analysis technology that aims to automatically detect
identify
and describe changes occurring in the same geographical area at different times. With the advancement of remote sensing and Artificial Intelligence (AI) technology
traditional data-driven and modal CD methods are evolving toward data-model-knowledge jointly driven direction to solve the land surface spatio-temporal CD problem in a variety of application fields in a more automatic
refined
and intelligent manner. This paper first summarizes existing problems in multitemporal remote sensing CD by analyzing the use of homogeneous and heterogenous data sources
developments from traditional to intelligent CD models
and challenges from theoretical to practical CD applications. Optical image CD is taken as an example
and the evolution of CD technology in the era of AI is examined
which can be summarized as three periods of data-driven CD
model-driven CD
and data-model-knowledge driven CD. Then
the characteristics and problems of each periods are discussed. Furthermore
for each of the three aspects (unsupervised
supervised
and weakly supervised)
the characteristics and trends in the development of traditional to cutting-edge CD techniques are discussed. In the future
one can focus on breaking through key issues such as the physical interpretability
generalization
and transferability of the CD models as well as their successful implementation in cross-data
cross-scene
and cross-domain applications.
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