一种基于新差异算子和纹理的SAR图像水体变化检测算法

doi:10.3969/j.issn.1006-2475.2020.04.015

计算机与现代化

一种基于新差异算子和纹理的SAR图像水体变化检测算法

(中国科学院电子学研究所,北京100190)

收稿日期:2019-09-12 出版日期:2020-04-22 发布日期:2020-04-24
作者简介:李玲玉(1994-),女,重庆人,硕士研究生,研究方向:SAR图像处理及应用,E-mail: 1114928966@qq.com; 张毅(1971-),男,上海人,研究员,博士生导师,研究方向:高速数字信号处理,合成孔径雷达信号处理新技术,合成孔径雷达系统设计,E-mail: zhangyi@mail.ie.ac.cn。
基金资助:
国家重点研发计划项目(2017YFB0502700)

A Water Change Detection Method of SAR Images Based on #br# New Difference Operator and Texture

(Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China)

Received:2019-09-12 Online:2020-04-22 Published:2020-04-24

摘要/Abstract

摘要： 针对多时相合成孔径雷达(SAR)变化检测中的孤立噪声点、需人工选择部分参数、信息利用不全等问题，提出一种基于新差异算子和纹理的SAR图像水体变化检测算法。一是根据SAR图像的特征，结合比率检测算子(LR)和最大似然比检测算子(LLR)，提出一种新的差异算子，放大非变化和变化区域的特点，然后根据新差异图像的相邻直方图比值图确定差异图像初始分割阈值。二是提出一种新的基于局部信息的模糊C均值聚类(FLICM)方法，该方法利用初始分割阈值得到初始聚类中心，然后提出基于纹理的FLICM(FLICM_texture)方法进一步将差异图像分为3类。三是根据差异图像分割的阈值将过渡区域再次分类。本文利用加拿大渥太华和瑞士伯尔尼市、印度金奈上空的SAR图像，展示了本文方法的优越性。渥太华地区的正确率(PCC)达到了98.00%，kappa系数达到了92.03%；伯尔尼地区PCC达到了99.66%，kappa系数达到了85.77%；金奈地区PCC达到了98.83%，kappa系数达到了84.96%。

关键词: SAR, 水体变化检测, 差异图算子, 纹理； FLICM, 金奈

Abstract: Aiming at the problems of isolated noise points, artificial selection of partial parameters and incomplete information utilization in multi-temporal Synthetic Aperture Radar (SAR) change detection, an improved water change detection method of SAR images based on new difference operator and texture is proposed. First, according to characteristics of SAR images, combining with Log Ratio (LR) operator and Logarithmic Likelihood Ratio (LLR) operator, a new difference operator is proposed to amplify the characteristics of unchanged and changed regions. Then, according to the ratio graph of the histogram at two adjacent gray level in the new difference image, the threshold of initial segmentation is determined. Second, a new Fuzzy Local Information C-Means (FLICM) clustering method is proposed. This method utilizes the threshold from the previous step to obtain the initial clustering center. Then the texture-based FLICM method (FLICM_texture) is proposed to divide the difference image into three categories. Third, this paper divides the transition region again according to the threshold obtained by the difference image. This paper utilizes the SAR images over Canadas Ottawa, the Switzerland’s Bern and Chennai to demonstrate the superiority of this method. The Percentage of Correct Classifications (PCC) of Ottawa is 98.00%, the kappa coefficient is 92.03%. In Bern, the PCC can reach 99.66% and the kappa coefficient is 85.77%. In Chennai, the PCC can reach 98.83% and the kappa coefficient is 84.96%.

Key words: SAR, water change detection, difference operator, texture, FLICM, Chennai

中图分类号:

TN958

李玲玉,张毅. 一种基于新差异算子和纹理的SAR图像水体变化检测算法[J]. 计算机与现代化, doi: 10.3969/j.issn.1006-2475.2020.04.015.

LI Ling-yu, ZHANG Yi. A Water Change Detection Method of SAR Images Based on #br# New Difference Operator and Texture[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2020.04.015.

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一种基于新差异算子和纹理的SAR图像水体变化检测算法

A Water Change Detection Method of SAR Images Based on #br# New Difference Operator and Texture

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