To date, although numerous methods of Change detection (CD) in remote sensing images have been proposed, accurately identifying changes is still a great challenge, due to the difficulties in effectively modeling the features from ground objects with different patterns. In this paper, a novel CD method based on the graph convolutional network (GCN) and multiscale object-based technique is proposed for both homogeneous and heterogeneous images. First, the object-wise high level features are obtained through a pre-trained U-net and the multiscale segmentations. Second, by treating each parcel as a node, the graph representations can be formed and then fed into the proposed multiscale graph convolutional network with each channel corresponding to one scale. The multiscale GCN propagates the label information from a small amount of labeled nodes to the other unlabeled ones. Finally, to comprehensively incorporate the information from the output channels of multiscale GCN, a fusion strategy is designed using the parent–child relationships between scales. Extensive experiments on optical, SAR and heterogeneous optical/SAR data sets demonstrate that the proposed method outperforms some state-of-the-art methods in both qualitative and quantitative evaluations.

迄今为止，虽然已经提出了许多遥感图像变化检测（CD）的方法，但由于难以对具有不同模式的地物特征进行有效建模，因此准确识别变化仍然是一个巨大的挑战。在本文中，针对同质和异质图像提出了一种基于图卷积网络（GCN）和基于多尺度对象的技术的新型 CD 方法。首先，通过预训练的 U-net 和多尺度分割获得面向对象的高级特征。其次，通过将每个包裹视为一个节点，可以形成图表示，然后将其输入到所提出的多尺度图卷积网络中，每个通道对应一个尺度。多尺度 GCN 将标签信息从少量标记节点传播到其他未标记节点。最后，为了全面整合来自多尺度 GCN 输出通道的信息，使用尺度之间的父子关系设计了融合策略。对光学、SAR 和异构光学/SAR 数据集的大量实验表明，所提出的方法在定性和定量评估方面都优于一些最先进的方法。