State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, National University of Defense Technology, Sanyi Avenue, Kaifu District, Changsha 410073, Hunan, China.
Sensors (Basel). 2019 Jan 14;19(2):304. doi: 10.3390/s19020304.
Superpixel methods are widely used in the processing of synthetic aperture radar (SAR) images. In recent years, a number of superpixel algorithms for SAR images have been proposed, and have achieved acceptable results despite the inherent speckle noise of SAR images. However, it is still difficult for existing algorithms to obtain satisfactory results in the inhomogeneous edge and texture areas. To overcome those problems, we propose a superpixel generating method based on pixel saliency difference and spatial distance for SAR images in this article. Firstly, a saliency map is calculated based on the Gaussian kernel function weighted local contrast measure, which can not only effectively suppress the speckle noise, but also enhance the fuzzy edges and regions with intensity inhomogeneity. Secondly, superpixels are generated by the local -means clustering method based on the proposed distance measure, which can efficiently sort pixels to different clusters. In this step, the distance measure is calculated by combining the saliency difference and spatial distance with a proposed adaptive local compactness parameter. Thirdly, post-processing is utilized to clean up small segments. The evaluation experiments on the simulated SAR image demonstrate that our proposed method dramatically outperforms four state-of-the-art methods in terms of boundary recall, under-segmentation error, and achievable segmentation accuracy under almost all of the experimental parameters at a moderate segment speed. The experiments on real-world SAR images of different sceneries validate the superiority of our method. The superpixel results of the proposed method adhere well to the contour of targets, and correctly reflect the boundaries of texture details for the inhomogeneous regions.
超像素方法广泛应用于合成孔径雷达（SAR）图像处理。近年来，提出了许多 SAR 图像的超像素算法，尽管 SAR 图像存在固有斑点噪声，但这些算法取得了可接受的结果。然而，现有的算法仍然难以在不均匀的边缘和纹理区域获得令人满意的结果。为了克服这些问题，我们提出了一种基于像素显着性差异和空间距离的 SAR 图像超像素生成方法。首先，基于高斯核函数加权局部对比度测度计算显着性图，不仅可以有效抑制斑点噪声，还可以增强模糊边缘和强度不均匀区域。其次，通过基于所提出的距离测度的局部均值聚类方法生成超像素，可以有效地将像素分类到不同的聚类中。在这一步中，距离测度通过结合显着性差异和空间距离以及一个提出的自适应局部紧致度参数来计算。然后，利用后处理来清理小片段。在模拟 SAR 图像上的评估实验表明，与四种最先进的方法相比，我们提出的方法在边界召回率、欠分割误差和几乎所有实验参数下可实现的分割精度方面都有显著提高，同时具有中等的分割速度。不同场景的真实 SAR 图像实验验证了该方法的优越性。该方法的超像素结果很好地贴合了目标的轮廓，正确反映了不均匀区域纹理细节的边界。
