Pixel-based change detection (PBCD) is imperfect because it lacks spatial correlation and can cause misdetection and salt and pepper noise. Comparatively, object-based change detection (OBCD) is dependent on the accuracy of the segmentation scale, where over-segmentation or under-segmentation of the image objects reduce accuracy. The fusion of PBCD and OBCD maps has great potential in dealing with spectral variability and texture complexity in very high-resolution (VHR) remote sensing images. It is difficult to solve the problem of uncertainty, which is caused by the inaccuracy of the multiple-change maps. Evidence theory based on Dempster–Shafer (DS) theory is an effective method for modeling uncertainty and taking advantage of multiple pieces of evidence. In this study, we proposed a scale-driven CD method incorporating DS evidence theory and majority voting rule to generate CD by combining multiscale OBCD results and PBCD results. Experiments carried out in four different regions using the Gaofen-2 imagery were used to test the proposed approach. We conducted numerous experiments to compare the proposed approach with prevalent CD approaches. Based on the results, the proposed approach achieves the best performance because it combines the benefits of pixel-based and object-based methods.

中文翻译：

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通过融合基于像素的变化检测，结合多尺度不确定性分析的超高分辨率遥感图像的基于对象的变化检测

基于像素的变化检测 (PBCD) 是不完美的，因为它缺乏空间相关性，并可能导致误检测和椒盐噪声。相比之下，基于对象的变化检测（OBCD）取决于分割尺度的准确性，其中图像对象的过度分割或欠分割会降低准确性。PBCD 和 OBCD 地图的融合在处理超高分辨率 (VHR) 遥感图像中的光谱可变性和纹理复杂性方面具有巨大潜力。由于多次变化的地图不准确导致的不确定性问题难以解决。基于 Dempster-Shafer (DS) 理论的证据理论是一种对不确定性建模和利用多条证据的有效方法。在这项研究中，我们提出了一种结合DS证据理论和多数投票规则的尺度驱动CD方法，通过结合多尺度OBCD结果和PBCD结果来生成CD。使用 Gaofen-2 图像在四个不同区域进行的实验被用来测试所提出的方法。我们进行了大量实验，将所提出的方法与流行的 CD 方法进行比较。基于结果，所提出的方法实现了最佳性能，因为它结合了基于像素和基于对象的方法的优点。