论文标题
使用双任务暹罗网络和半监督学习的城市变化检测
Urban Change Detection Using a Dual-Task Siamese Network and Semi-Supervised Learning
论文作者
论文摘要
在这项研究中，提出了一种半监督的学习（SSL）方法，用于改善双颞图像对检测的城市变化检测。所提出的方法适应了双任务暹罗差网络，该网络不仅可以预测差分解码器的变化，而且还可以通过语义解码器为这两个图像的片段建筑物进行段。首先，对体系结构进行了修改，以产生从语义预测得出的第二个更改预测。其次，采用SSL来改善监督的变更检测。对于未标记的数据，我们引入了一种损失，鼓励网络预测两个变化输出之间的一致变化。使用SPACENET7数据集对所提出的方法进行了有关城市变化检测的测试。与三个完全监督的基准相比，SSL取得了改善的结果。
In this study, a Semi-Supervised Learning (SSL) method for improving urban change detection from bi-temporal image pairs was presented. The proposed method adapted a Dual-Task Siamese Difference network that not only predicts changes with the difference decoder, but also segments buildings for both images with a semantics decoder. First, the architecture was modified to produce a second change prediction derived from the semantics predictions. Second, SSL was adopted to improve supervised change detection. For unlabeled data, we introduced a loss that encourages the network to predict consistent changes across the two change outputs. The proposed method was tested on urban change detection using the SpaceNet7 dataset. SSL achieved improved results compared to three fully supervised benchmarks.
