Fast Superpixel-Based Clustering Algorithm for SAR Image Segmentation

In this letter, we propose a fast superpixel-based clustering algorithm (FSC) for synthetic aperture radar (SAR) image segmentation. First, the SAR image is over-segmented into superpixels by our previously proposed edge-aware superpixel generation method with one iteration merging (ESOM). Second, based on the obtained superpixels, the number of clusters is automatically selected by the density peak (DP) algorithm and knee point method instead of manual specification. Finally, the modified
$k$
-means clustering with the generalized-likelihood ratio (GLR) dissimilarity is performed on the superpixels to generate the final segmentation result. Experimental results on two real SAR images show that the proposed method outperforms other state-of-the-art methods in terms of both segmentation accuracy and computational efficiency. Moreover, our method is free of clustering parameters and achieves automatic SAR image segmentation.