基于加权混合SPF主动轮廓的SAR图像水陆分割

沈嘉程; 韩斌; 鲍秉坤

doi:10.12263/DZXB.20230119

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基于加权混合SPF主动轮廓的SAR图像水陆分割

学术论文 | 更新时间：2025-12-11

- 基于加权混合SPF主动轮廓的SAR图像水陆分割
- Water-Land Segmentation in SAR Image Using Weighted Hybrid SPF Based Active Contour
- 电子学报 2024年52卷第6期页码：2000-2010
- 作者机构：
  
  1.南京邮电大学通信与信息工程学院，江苏南京 210003
  2.南京邮电大学计算机学院，江苏南京 210023
- 作者简介：
  
  [ "沈嘉程男，1999年1月出生，江苏常熟人，现为南京邮电大学在读硕士研究生，研究方向为遥感图像处理. E-mail: shenjiacheng_0115@163.com" ]
  [ "韩斌男，1990年2月出生，安徽马鞍山人，现为南京邮电大学副教授，研究方向为遥感图像处理、机器学习、目标检测等. E-mail: njupt.bh@foxmail.com" ]
  [ "鲍秉坤女，1982年4月出生，安徽六安人，现为南京邮电大学计算机学院、软件学院、网络空间安全学院副院长、教授、博士生导师，研究方向为多媒体计算、社交多媒体、计算机视觉、人工智能等. E-mail: bingkunbao@njupt.edu.cn" ]
- 基金信息：
  
  国家自然科学基金(62325206;61936005;62201281);江苏省自然科学基金(BK20220392);南京邮电大学引进人才自然科学研究启动基金(NY222004)
- DOI：10.12263/DZXB.20230119
  中图分类号： TP391;P237
- 收稿：2023-02-10，
  
  修回：2023-12-08，
  
  纸质出版：2024-06-25
- 稿件说明：
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沈嘉程,韩斌,鲍秉坤.基于加权混合SPF主动轮廓的SAR图像水陆分割[J].电子学报,2024,52(06):2000-2010.

SHEN Jia-cheng, HAN Bin, BAO Bing-kun.Water-Land Segmentation in SAR Image Using Weighted Hybrid SPF Based Active Contour[J].Acta Electronica Sinica, 2024, 52(06): 2000-2010.
沈嘉程,韩斌,鲍秉坤.基于加权混合SPF主动轮廓的SAR图像水陆分割[J].电子学报,2024,52(06):2000-2010. DOI：10.12263/DZXB.20230119

SHEN Jia-cheng, HAN Bin, BAO Bing-kun.Water-Land Segmentation in SAR Image Using Weighted Hybrid SPF Based Active Contour[J].Acta Electronica Sinica, 2024, 52(06): 2000-2010. DOI：10.12263/DZXB.20230119

摘要

针对现有主动轮廓模型（Active Contour Model，ACM）无法进行准确高效的SAR（Synthetic Aperture Radar）图像水陆分割的难题，提出一种基于加权混合符号压力（Signed Pressure Force，SPF）主动轮廓的新模型.该模型将中值拟合中心和均值拟合中心结合起来，建立新的混合符号压力函数，以抑制干扰边缘的影响.通过设计基于绝对中位差的自适应权重，实现自动调节内外拟合中心的比例系数，以更合理地描述灰度差异.此外，计算平滑后图像梯度信息构建边缘引导函数并引入到SPF函数中，以进一步提高分割性能和演化效率.在实际SAR图像上进行分割实验，结果表明：与相关实验模型相比，提出模型在保持高效的同时能获得更加准确的分割结果，其中Dice相似系数（Dice Similarity Coefficient，DSC）至少提高6%.

Abstract

To solve the problem that the existing active contour models (ACM) cannot accurately segment the synthetic aperture radar (SAR) water-land images

a new ACM based on the weighted hybrid signed pressure force is proposed. The median fitting center and mean fitting center are combined to establish a new hybrid signed pressure function

which can suppress the influence of interference edges. The adaptive weights based on the absolute median difference are designed to automatically adjust the coefficients of the inner and outer fitting centers

which can describe grayscale differences more reasonably. Moreover

the gradient information of the smoothed image is calculated to construct an edge guidance function and introduced into the SPF function to further improve segmentation performance and evolution efficiency. Segmentation experiments are conducted on real SAR images and the results show that compared with the related ACMs

the proposed model can perform accurate segmentation with high efficiency and stability

and the Dice similarity coefficient (DSC) is improved by at least 6%.

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Keywords

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