Remote Sensing Change Detection Based on Deep Belief Networks Optimized by Domain Knowledge

ZHANG Haiming; WANG Mingchang; CHEN Xueye; WANG Fengyan; YANG Guodong; GAO Su

doi:10.13203/j.whugis20190471

Volume 47 Issue 5

May 2022

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Geomatics and Information Science of Wuhan University > 2022 > 47(5): 762-768. > DOI: 10.13203/j.whugis20190471

ZHANG Haiming, WANG Mingchang, CHEN Xueye, WANG Fengyan, YANG Guodong, GAO Su. Remote Sensing Change Detection Based on Deep Belief Networks Optimized by Domain Knowledge[J]. Geomatics and Information Science of Wuhan University, 2022, 47(5): 762-768. DOI: 10.13203/j.whugis20190471

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Remote Sensing Change Detection Based on Deep Belief Networks Optimized by Domain Knowledge

1.
College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China
2.
Key Laboratory of Urban Land Resources Monitoring and Simulation, MNR, Shenzhen 518000, China
3.
Yunnan Provincial Mapping Institute, Kunming 650034, China

Funds:

The National Natural Science Foundation of China 41472243

the Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources KF-2018-03-020

the Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources KF-2019-04-080

the Open Fund of Key Laboratory of Land Subsidence Monitoring and Prevention, Ministry of Natural Resources KLLSMP201901

the Scientific Research Project of the 13th Five-Year Plan of Jilin Province Education Department JJKH20200999KJ

More Information

Author Bio:
ZHANG Haiming, master, specializes in deep learning change detection. E-mail: zhanghm18@mails.jlu.edu.cn
Corresponding author:
WANG Mingchang, PhD, professor. E-mail: wangmc@jlu.edu.cn
Received Date: December 24, 2019
Published Date: May 04, 2022

Graphical Abstract

Abstract

Abstract

Objectives A method of deep learning change detection with domain knowledge as an optimization strategy was proposed to improve the change detection precision of high-resolution remote sensing im‍ages.
Methods The improved change vector analysis algorithm and grey-level co-occurrence matrix algorithm were used to obtain the spectral and texture changes of images, and reasonable thresholds were set to divide the changed samples from the unchanged samples based on the spectral and texture change intensity maps. The pattern shape index and spectral knowledge in domain knowledge were introduced as an optimization strategy to filter the changed samples for obtaining high-quality training samples. The deep belief network model was constructed and trained, and the results of deep learning change detection were optimized by the optimization strategy to reduce the influence of "salt and pepper noise" and false change zones on the detection accuracy.
Results The Results of change detection experiments show that the accuracies of Gaofen-2 and IKONOS imageswere increased by 7.58% and 14.69% and the recall by 17.08% and 23.87%, respectively, while the false alarms and were decreased by 30.22% and 23.30% and the missing alarms by 17.08% and 23.87%, respectively.
Conclusions Compared with the method before the optimization strategy was adopted, the proposed method in this paper can effectively improve the precision of change detection, and it provides a new way of using remote sensing images to improve the precision of deep learning change detection.
- remote sensing,
- change detection,
- deep belief networks,
- domain knowledge,
- Gaofen-2(GF-2)

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References

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