基于改进全连接条件随机场的SAR影像变化检测

doi:10.6046/zrzyyg.2022205

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摘要

变化检测是遥感领域的重要研究方向。针对现有条件随机场变化检测技术的不足,通过改进全连接条件随机场(fully connected conditional random field, FCCRF),提出一种全新的合成孔径雷达(synthetic aperture Radar, SAR)影像变化检测方法。首先,对生成SAR差分影像的对比算法进行总结,将其划分为像素级、邻域级和超邻域级3个层级; 然后,选取对数比、邻域比和改进非局部图3种典型对比算法,生成3组互补差分影像; 最后,通过扩展FCCRF二元势函数的高斯核个数对FCCRF进行改进,并利用改进后FCCRF模型生成变化检测图。所提出变化检测技术能够综合利用2期SAR影像的原始影像、3组互补差分影像和影像全局空间信息。另外,本文通过提出一种简单有效的参数确定策略,使得FCCRF能够全自动进行变化检测。4组真实SAR影像数据的实验结果表明,本文方法可行有效。

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	董婷
	符潍奇
	邵攀
	高利鹏
	武昌东

关键词 ：遥感, 非监督变化检测, SAR影像, 差分影像, 条件随机场

Abstract：

Change detection is the research focus of remote sensing. To overcome the shortcomings of the existing conditional random field (CRF)-based change detection, this study proposed a novel change detection method for synthetic aperture Radar (SAR) images based on an improved fully connected CRF (FCCRF). Firstly, this study summarized the comparative algorithms for generating differential images from SAR images, which were divided into three levels, namely pixel, neighborhood, and super-neighborhood. Then, this study selected three typical comparative algorithms-log ratio (LR), neighborhood ratio (NR), and improved non-local graph (INLG)-to produce three sets of complementary differential images. Finally, this study improved the FCCRF by extending the number of Gaussian kernels of the pairwise potential function of FCCRF and generated the change detection maps using the improved FCCRF model. The change detection method proposed in this study integrated the two-phase original SAR images, three sets of complementary differential images, and the global spatial information of images. In addition, this study presented a simple and effective parameter determination strategy, which allows the FCCRF to perform the change detection automatically. Experimental results on four sets of real SAR image data confirmed the effectiveness of the change detection method proposed in this study.

Key words： remote sensing unsupervised change detection SAR image differential image conditional random field

收稿日期: 2022-05-20 出版日期: 2023-09-19

ZTFLH:

TP79

基金资助:国家自然科学基金项目“模糊拓扑空间下高分辨率遥感影像多尺度融合变化检测方法研究”(41901341);“基于倾斜摄影和浮动车轨迹数据的精细化城市车道提取方法”(42101469)

通讯作者: 邵攀(1985-),男,博士,副教授,主要从事遥感图像处理、变化检测、人工智能等研究。Email: panshao@whu.edu.cn。

作者简介: 董婷(1988-),女,博士,副教授,主要从事模式识别、遥感图像处理、3S技术等研究。Email: dongt@ctgu.edu.cn。

引用本文:

董婷, 符潍奇, 邵攀, 高利鹏, 武昌东. 基于改进全连接条件随机场的SAR影像变化检测[J]. 自然资源遥感, 2023, 35(3): 134-144.
DONG Ting, FU Weiqi, SHAO Pan, GAO Lipeng, WU Changdong. Detection of changes in SAR images based on an improved fully-connected conditional random field. Remote Sensing for Natural Resources, 2023, 35(3): 134-144.

链接本文:

https://www.gtzyyg.com/CN/10.6046/zrzyyg.2022205 或 https://www.gtzyyg.com/CN/Y2023/V35/I3/134

Fig.1 IFCCRF变化检测方法流程

Fig.2 主要SAR影像差分影像生成算法分类

Fig.3 数据源

Fig.4 数据1不同算法的变化检测结果

Fig.5 数据2不同算法的变化检测结果

Fig.6 数据3不同算法的变化检测结果

Fig.7 数据4不同算法的变化检测结果

Tab.1 4组数据变化检测的定量指标

Tab.2 F-FCM,F-FCCRF和IFCCRF的变化检测结果

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