SAR影像超像素分割，即将SAR影像中相似像素按照度量准则聚合为超像素的过程，超像素能一定程度体现图像的语义特征，可有效降低后续图像理解的难度，已成为影像分类、变化检测等算法重要的预处理步骤。然而，现有的SAR影像超像素分割算法多基于局部聚类方法实现，这类方法存在超像素种子点个数预定义、缺乏影像细节自适应性和多次迭代导致的耗时过多等不足。针对上述问题，本文提出了基于邻域特性的单次迭代超像素自适应分割算法ASSA，该算法基于高斯混合模型的种子点自适应调整策略，实现了超像素个数自适应确定，并确保了超像素内部的同质性；利用优先级队列和邻域特性，实现了单次迭代下的超像素分割；同时，ASSA算法使用高斯核函数和后处理两种策略进行了SAR影像噪声抑制。本文从可视化效果、定量指标和运行时间三方面对算法的有效性和高效性进行了评估。实验结果表明，相比于其他超像素分割算法，ASSA算法能够基于影像特性实现自适应超像素分割，提高分割效率的同时生成的超像素边界贴合度和紧凑度都较高。

objective：Superpixel segmentation offers significant advantages for information extraction from SAR images. First, it effectively reduces data volume and enhances the efficiency of subsequent applications. Second, it effectively reduces noise interference in SAR images, thereby improving data quality. Third, superpixel segmentation preserves the edge features of images, which is beneficial to the SAR image post-processing stages, such as deep learning-based classification. Lastly, the results of superpixel segmentation can be directly used as inputs for graph convolutional networks to explore the application of superpixel-based graph convolutional networks. As a result, SAR image superpixel segmentation has found extensive application in ship monitoring, water body extraction, and various other fields. Existing superpixel segmentation algorithms for SAR images predominantly rely on local clustering methods; however, they exhibit certain shortcomings including a predefined number of superpixels, limited adaptability, and the necessity for multiple iterations. To overcome these limitations, this paper proposes a novel adaptive superpixel segmentation algorithm called ASSA. This algorithm maximizes the benefits derived from Gaussian mixture models, neighborhood properties, and priority queues. Method：Firstly, this paper proposes an adaptive adjustment strategy for seeds to overcome the challenges associated with predefined number of superpixels and limited adaptability. The strategy is based on Gaussian mixture models, involving seed adjustment and generation using homogeneity discrimination criteria. Secondly, the algorithm solves the issue of multiple iterations by implementing single-iteration superpixel segmentation using neighborhood properties and priority queues under the neighborhood compulsory connection. Finally, the algorithm tackles severe speckle noise in SAR images employing a Gaussian kernel function to smooth the unmarked pixels and a post-processing algorithm to eliminate isolated superpixels. Result：In this paper, we used 9 sentinel-1 images to evaluate the proposed ASSA in terms of visualization effect, quantitative accuracy and runtime efficiency. The results show that, compared to existing superpixel segmentation algorithms, the proposed ASSA achieves higher boundary adherence and internal homogeneity while improving segmentation efficiency. In particular, the boundary recall rate is improved by 11.3% and 15.9% compared to SLIC and ESOM, respectively, while the under-segmentation error rate is reduced by 33.3% and 29.4%, respectively. Conclusion: this paper proposes a single-iteration superpixel adaptive segmentation algorithm based on neighborhood characteristics and adaptive adjustment strategy for seeds. This algorithm combines Gaussian mixture models with superpixel homogeneity discrimination to achieve adaptive segmentation. The experimental results demonstrate that the proposed ASSA algorithm is an effective and efficient method for SAR image superpixel segmentation.