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Change Detection in SAR Images Based on Deep Learning

  • Hatem Magdy KeshkEmail author
  • Xu-Cheng Yin
Original Paper
  • 155 Downloads

Abstract

Change detection in remote-sensing images is used to detect changes during different time periods on the surface of the Earth. Because of the advantages of synthetic aperture radar (SAR), which is not affected by time, weather or other conditions, change-detection technology based on SAR images has important research value. At present, this technology has attracted the attention of increasingly more researchers, and has also been used extensively in diverse fields, such as urban planning, disaster assessment, and forest early warning systems. Our objective in this paper is to combine both the change detection of SAR images with the deep neural networks to compare its efficiency with fuzzy clustering method and deep belief network. Our experiments, conducted on real data sets and theoretical analysis, indicates the advantages of the proposed method. Our results appear that proposed deep-learning algorithms can further improve the change-detection process.

Keywords

Change detection SAR Remote sensing Deep learning 

Notes

Acknowledgements

This study was partially supported by University of Science and Technology Beijing.

Author contributions

HMK conceived, designed, and performed the experiments; HMK and X-CY analyzed the data; HMK wrote the paper.

Compliance with Ethical Standards

Conflict of interest

The authors declare no conflict of interest.

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Copyright information

© The Korean Society for Aeronautical & Space Sciences 2019

Authors and Affiliations

  1. 1.University of Science and Technology BeijingBeijingChina
  2. 2.National Authority for Remote Sensing and Space ScienceCairoEgypt

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