基于图像增强和融合的SAR图像变化检测  

Change Detection in Synthetic Aperture Radar Images Based on Image Enhancement and Fusion

作　　者：贺金鑫[1] 赵锐敏 罗文宝 李青翼 刘瑞辰 HE Jinxin;ZHAO Ruimin;LUO Wenbao;LI Qingyi;LIU Ruichen(College of Earth Sciences,Jilin University,Changchun 130061,China;Heilongjiang Geological Exploration Institute,Sinochem General Administration of Geology and Mine,Haerbin 150040,China)

机构地区：[1]吉林大学地球科学学院,长春130061 [2]中化地质矿山总局黑龙江地质勘查院,哈尔滨150040

出　　处：《吉林大学学报（信息科学版）》2023年第2期217-226,共10页Journal of Jilin University（Information Science Edition）

基　　金：国家重点研发计划基金资助项目(2020YFA0714103);第六届吉林大学青年师生交叉学科培育基金资助项目(2020-JCXK-04)。

摘　　要：为提高合成孔径雷达(SAR:Synthetic Aperture Radar)图像变化检测的准确率和鲁棒性,提出了一种基于图像增强和融合的无监督SAR图像变化检测方法。为获得较好的背景噪声抑制、变化区域增强和边缘保持效果,在对原始SAR图像进行自适应图像增强的基础上构造对数比和均值比差异图,采用低频小波系数加权平均和按最小局部能量选取高频小波系数的融合策略对上述差异图进行图像融合。实验结果表明,融合后的差异图结合模糊局部信息C均值聚类在不同的数据集上均取得了较高的检测准确率和Kappa系数,鲁棒性较强,可广泛应用于SAR图像变化检测领域。In order to improve the accuracy and robustness of SAR(Synthetic Aperture Radar) image change detection, an unsupervised SAR image change detection method based on image enhancement and fusion is proposed. In order to obtain better effects of background noise suppression, change region enhancement and edge preservation, the log-ratio and mean-ratio differential image are constructed based on the adaptive image enhancement of the original SAR image. The differential image is fused by the fusion strategy of weighted average of low-frequency wavelet coefficients and selecting high-frequency wavelet coefficients according to the minimum local energy. The experimental results show that the fused differential image combined with fuzzy local information C-means clustering has achieved high detection accuracy and kappa coefficient on different data sets, and has strong robustness. It can be widely used in the field of SAR image change detection.

关 键 词：图像变化检测 图像增强 图像融合 模糊局部信息C均值聚类 合成孔径雷达 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]