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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.