Change detection in synthetic aperture radar (SAR) images is an essential task of remote sensing image analysis. However, the thresholding procedure is the main difficulty in change detection for a few changed areas for traditional change detection methods. In this article, we propose a novel change detection method for very few changed or even none changed areas. The proposed method contains three procedures: difference image (DI) generation, thresholding, and spatial analysis. In the second procedure, a new thresholding method called histogram fitting error minimization (HFEM) is proposed for a few changed areas. HFEM is derived under the assumption that the unchanged class in the absolute-valued DI follows the half-normal distribution, and the changed class follows the Gaussian distribution. In the spatial analysis procedure, a new conditional random fields (CRF) method based on half-normal distribution is proposed to model the mutual influences among image pixels. The proposed CRF method is called half-normal CRF (HNCRF). Experiments carried out on both synthetic datasets and four real SAR datasets demonstrate the superiority of our method. Not only a few changed datasets but datasets with lots of changes are used in the experiments. The kappa coefficients of the proposed method can reach up to ten times that of the traditional method under extreme conditions. The results prove that the proposed method outperforms the traditional methods in the case of a few changed areas. Meanwhile, the proposed method can get similar results compared with traditional methods under normal conditions.

中文翻译：

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值得信赖、可靠且轻量级的物联网隐私和数据完整性方法


合成孔径雷达（SAR）图像的变化检测是遥感图像分析的一项重要任务。然而，阈值处理过程是传统变化检测方法在少数变化区域进行变化检测的主要困难。在本文中，我们针对很少变化甚至没有变化的区域提出了一种新颖的变化检测方法。所提出的方法包含三个过程：差异图像（DI）生成、阈值处理和空间分析。在第二个过程中，针对一些变化的区域提出了一种称为直方图拟合误差最小化（HFEM）的新阈值方法。 HFEM 是在假设绝对值 DI 中未变化的类服从半正态分布、变化的类服从高斯分布的假设下推导的。在空间分析过程中，提出了一种基于半正态分布的条件随机场（CRF）方法来模拟图像像素之间的相互影响。所提出的CRF方法称为半正态CRF（HNCRF）。在合成数据集和四个真实 SAR 数据集上进行的实验证明了我们方法的优越性。实验中不仅使用了少量变化的数据集，而且还使用了变化较多的数据集。在极端条件下，该方法的kappa系数可以达到传统方法的10倍。结果证明，在少数变化区域的情况下，该方法优于传统方法。同时，在正常情况下，该方法可以得到与传统方法相似的结果。