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Remote sensing change detection technology in the Era of artificial intelligence： Inheritance， development and challenges

  • role： First author第一作者
  • Affiliation：

    College of Surveying and Geoinformatics, Tongji University, Shanghai 200092, China

  • Email：sicong.liu@tongji.edu.cn
  • Introduction：柳思聪，研究方向为遥感光谱探测与多时相变化检测。E-mail： sicong.liu@tongji.edu.cn
LIU Sicong1,  
  • Affiliation：

    College of Surveying and Geoinformatics, Tongji University, Shanghai 200092, China

DU Kecheng1,  
  • Affiliation：

    College of Surveying and Geoinformatics, Tongji University, Shanghai 200092, China

ZHENG Yongjie1,  
  • Affiliation：

    State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China

CHEN Jin2,  
  • Affiliation：

    School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China

DU Peijun3,  
  • Affiliation：

    College of Surveying and Geoinformatics, Tongji University, Shanghai 200092, China

TONG Xiaohua1

résumé

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.

mots-clés

remote sensing;change detection;multi-temporal analysis;artificial intelligence;machine learning;deep learning

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