In this article, we propose a new semisupervised method to detect the changes occurring in a geographical area after a major damage. We detect the changes by processing a pair of optical remote sensing images. The proposed method adopts a patch-based approach, whereby we use a Siamese convolutional neural network (S-CNN), trained with augmented data, to compare successive pairs of patches obtained from the input images. The main contribution of this work lies in developing an S-CNN training phase without resorting to class labels that are actually not available from the input images. We train the S-CNN using genuine and impostor patch-pairs defined in a semisupervised way from the input images. We tested the proposed change detection model on four real datasets and compared its performance to those of two existing models. The obtained results were very promising.

中文翻译：

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使用 Siamese CNN 检测未标记光学遥感数据的变化

在本文中，我们提出了一种新的半监督方法来检测重大损坏后地理区域发生的变化。我们通过处理一对光学遥感图像来检测变化。所提出的方法采用基于补丁的方法，我们使用使用增强数据训练的连体卷积神经网络 (S-CNN) 来比较从输入图像获得的连续补丁对。这项工作的主要贡献在于开发 S-CNN 训练阶段，而无需求助于实际上无法从输入图像中获得的类别标签。我们使用从输入图像中以半监督方式定义的真实和冒名顶替补丁对训练 S-CNN。我们在四个真实数据集上测试了所提出的变更检测模型，并将其性能与两个现有模型的性能进行了比较。