种子点自适应调整策略下的SAR影像超像素分割  

Adaptive superpixel segmentation of SAR images using an adaptive adjustment strategy for seeds

作　　者：赵腾 杜小平 严珍珍 朱俊杰 徐琛 范湘涛 ZHAO Teng;DU Xiaoping;YAN Zhenzhen;ZHU Junjie;XU Chen;FAN Xiangtao(Key Laboratory of Digital Earth Science,Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100094,China;International Research Center of Big Data for Sustainable Development Goals,Beijing 100094,China;University of Chinese Academy of Sciences,Beijing 100049,China)

机构地区：[1]中国科学院空天信息创新研究院数字地球重点实验室,北京100094 [2]可持续发展大数据国际研究中心,北京100094 [3]中国科学院大学,北京100049

出　　处：《遥感学报》2025年第1期290-299,共10页NATIONAL REMOTE SENSING BULLETIN

基　　金：国家自然科学基金(编号:41974108);广西创新驱动发展专项资金(编号:桂科AA20302022);国家重点研发计划(编号:2022YFC3800700);中国科学院A类战略性先导科技专项(编号:XDA19080101,XDA19080103)。

摘　　要：SAR影像超像素分割是将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2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3方面对提出的算法的有效性和高效性进行了评估。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内部同质性都较高。其中,边界召回率较SLIC和ESOM分别提高11.3%和15.9%,修正的欠分割错误率较SLIC和ESOM分别降低33.3%和29.4%。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,it preserves the edge features of images,which is beneficial to the SAR image postprocessing 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 study proposes a novel adaptive superpixel segmentation algorithm(ASSA).This algorithm maximizes the benefits derived from Gaussian mixture models,neighborhood properties,and priority queues.Method First,this study 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.Second,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.Lastly,the algorithm tackles severe speckle noise in SAR images by employing a Gaussian kernel function to smooth the unmarked pixels and a postprocessing algorithm to eliminate isolated superpixels.In this study,we use nine Sentinel-1 images to evaluate ASSA in terms of visuali

关 键 词：SAR 超像素分割 优先级队列 种子点自适应调整策略 高斯混合模型 

分 类 号：P237[天文地球—摄影测量与遥感] P2[天文地球—测绘科学与技术]