可变类空间约束高斯混合模型遥感图像分割

赵泉华; 石雪; 王玉; 李玉

doi:10.11959/j.issn.1000-436x.2017026

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可变类空间约束高斯混合模型遥感图像分割

学术论文 | 更新时间：2024-06-05

- 可变类空间约束高斯混合模型遥感图像分割
- Remote sensing image segmentation based on spatially constrained Gaussian mixture model with unknown class number
- 通信学报 2017年38卷第2期页码：34-43
- 作者机构：
  
  辽宁工程技术大学测绘与地理科学学院遥感科学与应用研究所，辽宁阜新 123000
- 作者简介：
  
  [ "赵泉华（1978-），女，河北承德人，博士，辽宁工程技术大学副教授、博士生导师，主要研究方向为随机几何、空间统计学、模糊集理论等在遥感图像建模、解译及其在海洋环境遥感中的应用。" ]
  [ "石雪（1992-），女，辽宁阜新人，辽宁工程技术大学硕士生，主要研究方向为影像信息提取理论与方法。" ]
  [ "王玉（1990-），女，辽宁本溪人，辽宁工程技术大学博士生，主要研究方向为图像处理。" ]
  [ "李玉（1963-），男，吉林长春人，博士，辽宁工程技术大学教授、博士生导师，主要研究方向为遥感图像处理。" ]
- 基金信息：
  
  国家自然科学基金资助项目(41301479);国家自然科学基金资助项目(41271435)
- DOI：10.11959/j.issn.1000-436x.2017026
  中图分类号： TP391.41
- 网络出版日期：2017-02，
  
  纸质出版日期：2017-02-25
- 稿件说明：
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赵泉华, 石雪, 王玉, 等. 可变类空间约束高斯混合模型遥感图像分割[J]. 通信学报, 2017,38(2):34-43.

Quan-hua ZHAO, Xue SHI, Yu WANG, et al. Remote sensing image segmentation based on spatially constrained Gaussian mixture model with unknown class number[J]. Journal on communications, 2017, 38(2): 34-43.
赵泉华, 石雪, 王玉, 等. 可变类空间约束高斯混合模型遥感图像分割[J]. 通信学报, 2017,38(2):34-43. DOI： 10.11959/j.issn.1000-436x.2017026.

Quan-hua ZHAO, Xue SHI, Yu WANG, et al. Remote sensing image segmentation based on spatially constrained Gaussian mixture model with unknown class number[J]. Journal on communications, 2017, 38(2): 34-43. DOI： 10.11959/j.issn.1000-436x.2017026.

摘要

针对传统高斯混合模型（GMM

Gaussian mixture model）难以自动获取类属数和对噪声敏感问题，提出了一种基于可变类空间约束GMM的遥感图像分割方法。首先在构建的GMM中，将像素类属性建模为马尔可夫随机场（MRF

Markov random field），并在此基础上定义其先验概率；结合邻域像素类属性的后验概率和先验概率，定义噪声平滑因子，以提高算法的抗噪性；在参数求解过程中，分别采用可逆跳变马尔可夫链蒙特卡罗（RJMCMC

reversible jump Markov chain Monte Carlo）方法和最大似然（ML

maximum likelihood）方法估计类属数和模型参数；最后以最小化噪声平滑因子为准则获取最终分割结果。为了验证提出的分割方法，分别对模拟图像和全色遥感图像进行了可变类分割实验。实验结果表明提出方法的可行性和有效性。

Abstract

In view of the traditional Gaussian mixture model (GMM)

it was difficult to obtain the number of classes and sensitive to the noise.A remote sensing image segmentation method based on spatially constrained GMM with unknown number of classes was proposed.First

in the built GMM

prior probability that represented the membership between a pixel and one class was modeled as a Markov random field (MRF).In order to improve the sensitivity of noise

the smoothing factor was defined by combining the a posterior probability and the prior probability of neighboring pixels.For estimating the number of classes and the parameters of model

the reversible jump Markov chain Monte Carlo (RJMCMC) and maximum likelihood (ML) estimation were employed

respectively.Finally

by minimizing the smoothing factor the final segmentation was obtained.In order to verify the proposed segmentation method

the synthetic and real panchromatic images were tested.The experimental results show that the proposed method is feasible and effective.

关键词

Keywords

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