In recent years, regional algorithms have shown great potential in the field of synthetic aperture radar (SAR) image segmentation. However, SAR images have a variety of landforms and a landform with complex texture is difficult to be divided as a whole. Due to speckle noise, traditional over-segmentation algorithm may cause mixed superpixels with different labels. They are usually located adjacent to two areas or contain more noise. In this paper, a new semantic segmentation method of SAR images based on texture complexity analysis and key superpixels is proposed. Texture complexity analysis is performed and on this basis, mixed superpixels are selected as key superpixels. Specifically, the texture complexity of the input image is calculated by a new method. Then a new superpixels generation method called neighbourhood information simple linear iterative clustering (NISLIC) is used to over-segment the image. For images with high texture complexity, the complex areas are first separated and key superpixels are selected according to certain rules. For images with low texture complexity, key superpixels are directly extracted. Finally, the superpixels are pre-segmented by fuzzy clustering based on the extracted features and the key superpixels are processed at the pixel level to obtain the final result. The effectiveness of this method has been successfully verified on several kinds of images. Comparing with the state-of-the-art algorithms, the proposed algorithm can more effectively distinguish different landforms and suppress the influence of noise, so as to achieve semantic segmentation of SAR images.

中文翻译：

---

基于纹理复杂度分析和关键超像素的SAR图像语义分割

近年来，区域算法在合成孔径雷达（SAR）图像分割领域显示出巨大潜力。然而，SAR图像具有多种地形，并且具有复杂纹理的地形难以整体划分。由于斑点噪声，传统的过度分割算法可能会导致带有不同标签的混合超像素。它们通常位于两个区域附近或包含更多噪声。提出了一种基于纹理复杂度分析和关键超像素的SAR图像语义分割方法。进行纹理复杂度分析，并在此基础上选择混合的超像素作为关键超像素。具体地，通过新方法计算输入图像的纹理复杂度。然后使用一种新的超像素生成方法，称为邻域信息简单线性迭代聚类（NISLIC）对图像进行过分割。对于具有高纹理复杂度的图像，首先将复杂区域分开，然后根据某些规则选择关键的超像素。对于纹理复杂度较低的图像，直接提取关键的超像素。最后，基于提取的特征，通过模糊聚类对超像素进行预分割，并对关键超像素进行像素级处理，以获得最终结果。该方法的有效性已在多种图像上成功验证。与最新算法相比，该算法可以更有效地区分不同的地形并抑制噪声的影响，从而实现SAR图像的语义分割。