Education, Science, Technology, Innovation and Life
Open Access
Sign In

SAR Image Change Detection Research: A Review

Download as PDF

DOI: 10.23977/geors.2022.050103 | Downloads: 5 | Views: 625

Author(s)

Huiqin Chen 1, Fujun Zhao 2, Zeyuan Gu 1

Affiliation(s)

1 School of Computer and Information Engineering, Heilongjiang University of Science and Technology, Harbin, China
2 School of Information & Intelligence Engineering, University of Sanya, Sanya, China

Corresponding Author

Huiqin Chen

ABSTRACT

SAR image change detection technology has always been a research hotspot and difficulty in remote sensing, which has attracted many scholars at home and abroad to conduct in-depth research on it. However, there is still a lack of detection methods with strong generalization, high accuracy, and complete automation. With the development of synthetic aperture radar, the SAR change detection technology system is also constantly updated and evolved. An in-depth analysis of the research status of SAR image change detection technology at home and abroad is carried out to supplement the latest applications of deep learning methods in SAR image change detection methods in recent years. At the same time, focusing on the whole process of SAR image change detection, the theory and methods of SAR image change detection are further categorized and summarized from three aspects: SAR image imaging mechanism, SAR image change detection technology, and accuracy evaluation, and the challenges and future development trend of SAR image change detection are pointed out, to promote the further development of SAR image change detection research.

KEYWORDS

SAR Image Change Detection, Deep Learning, Research Review

CITE THIS PAPER

Huiqin Chen, Fujun Zhao, Zeyuan Gu, SAR Image Change Detection Research: A Review. Geoscience and Remote Sensing (2022) Vol. 5: 16-21. DOI: http://dx.doi.org/10.23977/geors.2022.050103.

REFERENCES

[1] SINGH A. Review article digital change detection techniques using remotely-sensed data [J]. International journal of remote sensing, 1989, 10(6): 989-1003.
[2] WHITE R G. Change detection in SAR imagery [J]. International Journal of remote sensing, 1991, 12(2): 339-360.
[3] HACHICHA S, CHAABANE F. On the SAR change detection review and optimal decision [J]. International Journal of Remote Sensing, 2014, 35(5): 1693-1714.
[4] LIU MingXu, Zhang Yonghong. Review of SAR Image Change Detection [J]. Geospatial Information, 2014, 12(03): 36-40+6.
[5] Chen FuLong, Zhang Hong, Wang Chao. The Art in SAR Chang Detection-A Systematic Review [J]. Remote Sensing Technology and Application, 2007, 22(1): 109–115.
[6] Yang Jing,Shen Chen,Wang XianBin. A Review of SAR Image Change Detection Technology Research [J]. China Water Transport, 2011(1): 109–111.
[7] Wu Tao, Chen Xi, Niu Lei, et al. Latest Development of Research on Unsupervised Change Detectionin SAR Images [J]. Remote Sensing Information, 2013(1): 110–118.
[8] Chen BeiBei. SAR Images Change Detection Based on Deep Learning [D]. Xi'an: XIDIAN UNIVERSITY, 2020.
[9] Wu Fei. SAR Images Change Detection Based on Generative Adversarial Network and Non-Local Neural Network [D]. Xi'an: XIDIAN UNIVERSITY, 2019.
[10] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. Imagenet classification with deep convolutional neural networks [J]. Advances in neural information processing systems, 2012, 25.
[11] ZHANG Xiaoyu, ZHAO Fengjun, LI Ling. Change Detection in SAR Image Based on Multi-Channel Features [J]. Radar Science and Technology, 2017, 15(05): 509-518.
[12] REN Qiuru, YANG Wenzhong, WANG Chuanjian, WEI Wenyu, QIAN Yunyun. Review of remote sensing image change detection [J]. Journal of computer Application, 2021, 41(08): 2294-2305.
[13] RIGNOT E J M, VAN ZYL J J. Change detection techniques for ERS-1 SAR data [J]. IEEE Transactions on Geoscience and Remote sensing, 1993, 31(4): 896-906,
[14] VILLASENORENSOR J D, FLATLAND D R, HINZMAN L D. Change detection on Alaska's North Slope using repeat-pass ERS-1 SAR images [J]. IEEE Transactions on Geoscience and Remote Sensing, 1993, 31(1): 227-236.
[15] YAOGUO ZHENG, XIANGRONG ZHANG, BIAO HOU, et al. Using combined difference image and k-means clustering for SAR image change detection [J]. IEEE Geoscience and Remote Sensing Letters, 2013, 11(3): 691-695.
[16] WANG J, GAO F, DONG J. Change detection from SAR images based on deformable residual convolutional neural networks[C]. Proceedings of the 2nd ACM International Conference on Multimedia in Asia. 2021: 1-7.
[17] GONG M, YANG H, ZHANG P. Feature learning and change feature classification based on deep learning for ternary change detection in SAR images [J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2017, 129: 212-225.
[18] MOU L, BRUZZONE L, ZHU XX. Learning spectral-spatial-temporal features via a recurrent convolutional neural network for change detection in multispectral imagery [J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 57(2): 924-935.
[19] JI Shunping, TIAN Siqi, ZHANG Chi. Urban Land Cover Classification and Change Detection Using Fully Atrous Convolutional Neural Network [J]. Geomatics and Information Science of Wuhan University, 2020, 45(2): 233-241.
[20] GONG M, ZHAO J, LIU J, et al. Change detection in synthetic aperture radar images based on deep neural networks [J]. IEEE transactions on neural networks and learning systems, 2015, 27(1): 125-138.
[21] CHEN L, ZHANG H, XIAO J, et al. SCA-CNN: Spatial and channel-wise attention in convolutional networks for image captioning [C]. Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 5659-5667.
[22] WOO S, PARK J, LEE J Y, et al. CBAM: Convolutional block attention module [C]. Proceedings of the European conference on computer vision (ECCV). 2018: 3-19.
[23] FU J, LIU J, TIAN H, et al. Dual attention network for scene segmentation [C]. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019: 3146-3154.
[24] WANG R, DING F, JIAO L, et al. Lightweight convolutional neural network for bitemporal SAR image change detection [J]. Journal of Applied Remote Sensing, 2020, 14(3): 036501.
[25] OLOFSSON P, FOODY G M, HEROLD M, et al. Good practices for estimating area and assessing accuracy of land change [J]. Remote sensing of Environment, 2014, 148: 42-57.

Downloads: 504
Visits: 52393

Sponsors, Associates, and Links


All published work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2016 - 2031 Clausius Scientific Press Inc. All Rights Reserved.