Although various methods can effectively segment synthetic aperture radar (SAR) images, we found that the method combining superpixel and image edge information can get better results. To solve the problem that common SAR image segmentation methods often segment pixels incorrectly in edge region, a superpixel boundary-based edge description algorithm (SpBED) is proposed. First, an edge detection method with three edge detectors is used. Therefore, accurate strong edges of SAR images can be extracted, and false edges that are easy to appear in a single detection method can be effectively eliminated. Then the weak edges of the image are extracted by superpixel generation algorithm. The extracted weak edges can supplement the edge information that is difficult to extract by edge detection. Superpixel boundaries are also used to carry the strong edges, so that the strong and weak edges can be completely represented by superpixel boundaries. Finally, boundary constraint superpixel smoothing is used to reduce the effects of noise, and k-means algorithm is performed on superpixels. Since edge information is carried by superpixels, it effectively guarantees the segmentation accuracy in edge region. Compared with seven state-of-the-art algorithms, segmentation results on simulated images and real images demonstrate the effectiveness of the proposed SpBED.

中文翻译：

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基于超像素边界的SAR图像分割边缘描述算法

虽然各种方法都可以有效分割合成孔径雷达（SAR）图像，但我们发现结合超像素和图像边缘信息的方法可以获得更好的结果。针对常见的SAR图像分割方法往往对边缘区域的像素进行错误分割的问题，提出了一种基于超像素边界的边缘描述算法(SpBED)。首先，使用具有三个边缘检测器的边缘检测方法。因此，可以准确提取出SAR图像的强边缘，有效消除单一检测方法中容易出现的虚假边缘。然后通过超像素生成算法提取图像的弱边缘。提取的弱边缘可以补充边缘检测难以提取的边缘信息。超像素边界也用于承载强边缘，使得强弱边缘可以完全用超像素边界来表示。最后，使用边界约束超像素平滑来减少噪声的影响，并对超像素进行k-means算法。由于边缘信息由超像素承载，有效保证了边缘区域的分割精度。与七种最先进的算法相比，模拟图像和真实图像的分割结果证明了所提出的 SpBED 的有效性。