Synthetic aperture radar (SAR) coherent change detection (CCD) predominantly uses the degree of coherence or similar change metrics as a measurement of the changes that have occurred between two data collections. Many existing coherence estimators have shown some performances for change detection, but are still relatively limited, because the change areas do not stand out well from all decorrelation areas due to low clutter-to-noise ratio (CNR) and volume scattering. Besides, many estimators require the equal-variance assumption between two SAR images of the same scene. However, the assumption is less likely to be met in areas with significant intensity differences, especially in change regions. In this article, a novel weighted coherence estimator is proposed to address these problems. The estimator is derived based on the statistical characteristics of SAR images using the maximum-likelihood (ML) principle. The introduction of weight parameters not only makes the estimator no longer need to satisfy the equal-variance assumption but also combines the advantages of coherent and noncoherent algorithms to a certain extent, because the weights are closely related to the ratio change statistic. Experiments on simulated and real SAR image pairs demonstrate the effectiveness of the proposed estimator in highlighting change areas and the boundaries between change and other areas in theory and practice.

中文翻译：

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SAR相干变化检测的加权相干估计器


合成孔径雷达 (SAR) 相干变化检测 (CCD) 主要使用相干程度或类似的变化度量来衡量两个数据集合之间发生的变化。许多现有的相干估计器已经显示出一些变化检测性能，但仍然相对有限，因为由于低杂波噪声比（CNR）和体积散射，变化区域并不能从所有去相关区域中脱颖而出。此外，许多估计器需要同一场景的两个 SAR 图像之间的等方差假设。然而，在强度差异显着的地区，特别是在变化区域，该假设不太可能得到满足。在本文中，提出了一种新颖的加权相干估计器来解决这些问题。该估计器是根据 SAR 图像的统计特性，使用最大似然（ML）原理推导出来的。权重参数的引入不仅使估计器不再需要满足等方差假设，而且在一定程度上结合了相干算法和非相干算法的优点，因为权重与比率变化统计量密切相关。模拟和真实 SAR 图像对的实验证明了所提出的估计器在理论和实践中突出变化区域以及变化与其他区域之间的边界的有效性。