Superpixel Generation for SAR Imagery Based on Fast DBSCAN Clustering With Edge Penalty

In this article, we propose an adaptive superpixel generation algorithm for synthetic aperture radar (SAR) imagery, which is implemented based on fast density-based spatial clustering of applications with noise (DBSCAN) clustering and superpixel merging with edge penalty. The superpixel generation algorithm consists of two stages, i.e., fast pixel clustering and superpixel merging. In the clustering stage, we define a new adaptive pixel dissimilarity measure for SAR image and then optimize the DBSCAN strategy, which considers the edge information and can achieve rapid clustering. In the merging stage, based on the initial superpixels, a new superpixel dissimilarity measure is defined, which can merge the small local superpixels into their neighborhood superpixels, making the final superpixel segmentation results compact and regular. Experimental results on two simulated and two real SAR images demonstrate that our method outperforms the state-of-the-art superpixel generation methods in terms of both efficiency and accuracy. The superpixel segmentation accuracy of our method is 5–10% higher and the time cost is 10–40% lower than other methods. Since the superpixel segmentation result can be used as a preprocessing stage for the SAR data interpretation applications, superpixel-based and pixel-based classification results with two real SAR images are also used for comparison, which can validate the advantages of our proposed method.