A Novel Approach for SAR Target Detection Based on Unsupervised Complex-Valued Extreme Learning Machine

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.