Change detection for high-resolution remote sensing images is more and more widespread in the application of monitoring the Earth’s surface. However, on the one hand, the ground truth could facilitate the distinction between changed and unchanged areas, but it is hard to acquire them. On the other hand, due to the complexity of remote sensing images, it is difficult to extract features of difference, let alone the construction of the classification model that performs change detection based on the features of difference in each pixel pair. Aiming at these challenges, this paper proposes a weakly supervised change detection method based on edge mapping and Stacked Denoising Auto-Encoders (SDAE) network called EM-SDAE. We analyze the difference in edge maps of bi-temporal remote sensing images to acquire part of the ground truth at a relatively low cost. Moreover, we design a neural network based on SDAE with a deep structure, which extracts the features of difference so as to efficiently classify changed and unchanged regions after being trained with the ground truth. In our experiments, three real sets of high-resolution remote sensing images are employed to validate the high efficiency of our proposed method. The results show that accuracy can even reach up to 91.18% with our method. In particular, compared with the state-of-the-art work (e.g., IR-MAD, PCA-k-means, CaffeNet, USFA, and DSFA), it improves the Kappa coefficient by 27.19% on average.

中文翻译：

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基于边缘映射和SDAE网络的高分辨率遥感影像弱监督变化检测

在监视地球表面的应用中，高分辨率遥感影像的变化检测越来越广泛。但是，一方面，地面真理可以促进对变化和不变区域之间的区分，但是很难获得它们。另一方面，由于遥感图像的复杂性，难以提取差异特征，更不用说基于每个像素对中的差异特征执行变化检测的分类模型的构造。针对这些挑战，本文提出了一种基于边缘映射和称为EM-SDAE的堆叠式降噪自动编码器（SDAE）网络的弱监督变化检测方法。我们分析了双时相遥感图像边缘图的差异，以相对较低的成本获得了部分地面真相。此外，我们设计了一种基于SDAE的具有深层结构的神经网络，该神经网络提取差异的特征，以便在经过地面真理训练后有效地对变化和未变化的区域进行分类。在我们的实验中，使用了三组高分辨率的遥感图像的真实集来验证我们提出的方法的高效率。结果表明 使用三个真实的高分辨率遥感影像集来验证我们提出的方法的高效率。结果表明 使用三个真实的高分辨率遥感影像集来验证我们提出的方法的高效率。结果表明使用我们的方法，准确度甚至可以达到91.18％。特别是，与最新技术（例如IR-MAD，PCA-k-means，CaffeNet，USFA和DSFA）相比，它平均将Kappa系数提高了27.19％。