Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/88130
| Title: | Change detection based on artificial intelligence : state-of-the-art and challenges | Authors: | Shi, WZ Zhang, M Zhang, R Chen, SX Zhan, Z |
Issue Date: | 2-May-2020 | Source: | Remote sensing, 2 May 2020, v. 12, no. 10, 1688, p. 1-35 | 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. | Keywords: | Artificial intelligence Change detection Remote sensing Deep learning Neural network Unsupervised learning SAR Hyperspectral Multispectral Street view |
Publisher: | Molecular Diversity Preservation International (MDPI) | Journal: | Remote sensing | EISSN: | 2072-4292 | DOI: | 10.3390/rs12101688 | Rights: | © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). The following publication Shi, W.; Zhang, M.; Zhang, R.; Chen, S.; Zhan, Z. Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges. Remote Sens. 2020, 12, 1688 is available at https://dx.doi.org/10.3390/rs12101688 |
| Appears in Collections: | Journal/Magazine Article |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Shi_Change_Detection_Artificial.pdf | 4.71 MB | Adobe PDF | View/Open |
Access
View full-text via PolyU eLinks 
Page views
62
Last Week
1
1
Last month
Citations as of Jun 4, 2023
Downloads
208
Citations as of Jun 4, 2023
SCOPUSTM
Citations
189
Citations as of Jun 2, 2023
WEB OF SCIENCETM
Citations
163
Citations as of Jun 1, 2023
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.