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。
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.
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