SSN: Stockwell Scattering Network for SAR Image Change Detection

Gong Chen, Yanan Zhao, Yi Wang, Kim Hui Yap

Research output: Journal article publicationJournal articleAcademic researchpeer-review

4 Citations (Scopus)


Recently, synthetic aperture radar (SAR) image change detection has become an interesting yet challenging direction due to the presence of speckle noise. Although both traditional and modern learning-driven methods attempted to overcome this challenge, deep convolutional neural networks (DCNNs)-based methods are still hindered by the lack of interpretability and the requirement of large computation power. To overcome this drawback, wavelet scattering network (WSN) and Fourier scattering network (FSN) are proposed. Combining respective merits of WSN and FSN, we propose Stockwell scattering network (SSN) based on Stockwell transform (ST), which is widely applied against noisy signals and shows advantageous characteristics in speckle reduction. The proposed SSN provides noise-resilient feature representation and obtains state-of-the-art performance in SAR image change detection as well as high computational efficiency. Experimental results on three real SAR image datasets demonstrate the effectiveness of the proposed method.

Original languageEnglish
Article number4001405
Pages (from-to)1-5
JournalIEEE Geoscience and Remote Sensing Letters
Publication statusPublished - Jan 2023
Externally publishedYes


  • Image change detection
  • low computation power
  • noise robust
  • Stockwell scattering network (SSN)

ASJC Scopus subject areas

  • Geotechnical Engineering and Engineering Geology
  • Electrical and Electronic Engineering


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