Abstract
Change detection based on remote sensing (RS) data is an important method of detecting changes on the Earth’s surface and has a wide range of applications in urban planning, environmental monitoring, agriculture investigation, disaster assessment, and map revision. In recent years, integrated artificial intelligence (AI) technology has become a research focus in developing new change detection methods. Although some researchers claim that AI-based change detection approaches outperform traditional change detection approaches, it is not immediately obvious how and to what extent AI can improve the performance of change detection. This review focuses on the state-of-the-art methods, applications, and challenges of AI for change detection. Specifically, the implementation process of AI-based change detection is first introduced. Then, the data from different sensors used for change detection, including optical RS data, synthetic aperture radar (SAR) data, street view images, and combined heterogeneous data, are presented, and the available open datasets are also listed. The general frameworks of AI-based change detection methods are reviewed and analyzed systematically, and the unsupervised schemes used in AI-based change detection are further analyzed. Subsequently, the commonly used networks in AI for change detection are described. From a practical point of view, the application domains of AI-based change detection methods are classified based on their applicability. Finally, the major challenges and prospects of AI for change detection are discussed and delineated, including (a) heterogeneous big data processing, (b) unsupervised AI, and (c) the reliability of AI. This review will be beneficial for researchers in understanding this field.
Highlights
Change detection is the process of identifying differences in the state of an object or phenomenon by observing it at different times [1]
Most artificial intelligence (AI)-based change detection methods are based on several synthetic aperture radar (SAR) data sets that contain limited types of changes, e.g., the Bern dataset, the Ottawa dataset, the Yellow River dataset, and the Mexico dataset [24,103], which cannot meet the needs of change detection in areas with complex land cover and various change types
This review presents the latest methods, applications, and challenges of the AI-based change detection techniques
Summary
Change detection is the process of identifying differences in the state of an object or phenomenon by observing it at different times [1]. This paper provides a deep review of the application of AI technologies in RS change detection processing It focuses on the state-of-the-art methods, applications, and challenges of AI for change detection in multi-temporal data. By systematically reviewing and analyzing the process of AI-based change detection methods, we summarize their general frameworks in a practical way, which can help to design change detection approaches in the future.
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