Histogram-based perceptual hash algorithm for synthetic aperture radar image segmentation

Abstract

To speed up the segmentation procedure and improve the segmentation quality of synthetic aperture radar (SAR) image, this paper presents an adaptive unsupervised SAR image segmentation method, which is integrated in the grayscale histogram and perceptual hash algorithm. The final clusters can be acquired self-adaptively without predefining. The two stages of the algorithm are discussed in details: histogram-based preprocessing and region merging. In preprocessing, histogram-based gray-level reduction is adopted to generate the set of initial segmented regions to address the problem of over-segmentation. When merging regions, the similarity of different regions can be obtained by comparing their different fingerprints calculated in the perceptual hash algorithm, which is the kernel of the whole SAR image segmentation method. The application of the perceptual hash algorithm for image segmentation can reduce the computational complexity and improve the efficiency of region merging. Experimental simulations and theoretical analysis are used to demonstrate the effectiveness of this algorithm.