loading page

A Novel Approach for SAR Target Detection Based on Unsupervised Complex-Valued Extreme Learning Machine
  • Qinglong Hua,
  • Yun Zhang,
  • Yicheng Jiang
Qinglong Hua
Harbin Institute of Technology
Author Profile
Yun Zhang
Harbin Institute of Technology

Corresponding Author:[email protected]

Author Profile
Yicheng Jiang
Harbin Institute of Technology
Author Profile


Strong clutter seriously affects target-of-interest detection in synthetic aperture radar (SAR) images. This letter proposes an unsupervised target detection method (U-TDM) based on a complex-valued extreme learning machine (CV-ELM), the essence of which is to transform the problem of target detection into a pixel binary classification problem. The SAR image is first divided into several unlabeled patches, and fuzzy c-means (FCM) is used to construct the reference target patch set and the clutter patch set. Based on these two patch sets, CV-ELM is used to classify the neighboring patch of the pixel to be detected. Since the pixel intensity and distribution of target-of-interest and clutter are different, unsupervised pixel classification could be realized without ground-truth through U-TDM. Experimental results on GF-3 data and Sentinel-1 data show the efficiency of the proposed method in target detection with a heterogeneous clutter environment.
31 Jul 2023Submitted to Electronics Letters
01 Aug 2023Submission Checks Completed
01 Aug 2023Assigned to Editor
01 Aug 2023Reviewer(s) Assigned
13 Aug 2023Review(s) Completed, Editorial Evaluation Pending
16 Aug 2023Editorial Decision: Revise Major
06 Sep 20231st Revision Received
07 Sep 2023Submission Checks Completed
07 Sep 2023Assigned to Editor
07 Sep 2023Review(s) Completed, Editorial Evaluation Pending
07 Sep 2023Reviewer(s) Assigned
09 Sep 2023Editorial Decision: Accept