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SAR image change detection based on Gabor wavelets and convolutional wavelet neural networks

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Abstract

Synthetic aperture radar (SAR) image change detection technology is of great significance. In the existing convolutional wavelet neural networks (CWNN) based SAR image change detection methods, the precision of preclassification is not high. The precision of preclassification will affect the performance of the network, and thus affect the accuracy of image change detection. In order to further improve the accuracy of change detection, the method based on Gabor wavelets and convolutional wavelet neural networks (GWCWNN) is applied to SAR image change detection in this paper. This method combines Gabor wavelets and fuzzy C-means clustering algorithm to provide high precision training samples for the networks, so as to improve the accuracy of image change detection. The results on three real data sets respectively show that the proposed method is better than the existing four methods.

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Acknowledgments

This work was supported by the Special Fund for Basic Scientific Research of Central Colleges in Chang’an University (310812163504 and 300102129202).

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Authors and Affiliations

  1. Chang’an University, College of Science, Middle, Erhuannan Road, Xi’an, 710064, Shaanxi, China

    Wen Yi, Shijie Wang, Nannan Ji, Changpeng Wang, Yuzhu Xiao & Xueli Song

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  1. Wen Yi
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  2. Shijie Wang
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  3. Nannan Ji
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  4. Changpeng Wang
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  5. Yuzhu Xiao
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Correspondence to Yuzhu Xiao or Xueli Song.

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Yi, W., Wang, S., Ji, N. et al. SAR image change detection based on Gabor wavelets and convolutional wavelet neural networks. Multimed Tools Appl 82, 30895–30908 (2023). https://doi.org/10.1007/s11042-023-15106-5

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  • DOI: https://doi.org/10.1007/s11042-023-15106-5

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