Earth Imagery Segmentation on Terrain Surface with Limited Training Labels: A Semi-supervised Approach based on Physics-Guided Graph Co-Training
Earth Imagery Segmentation on Terrain Surface with Limited Training Labels: A Semi-supervised Approach based on Physics-Guided Graph Co-Training
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DOI：
10.1145/3481043
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发表时间：
2022-01
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通讯作者：
Wenchong He;Arpan Man Sainju;Zhe Jiang;Da Yan;Yang Zhou
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作者：
Wenchong He;Arpan Man Sainju;Zhe Jiang;Da Yan;Yang Zhou
鉴于地面表面上具有光谱特征的地球图像，本文根据解释性特征和表面拓扑研究了表面分割。这个问题在许多时空和时空应用中很重要，例如水文学中的洪水范围映射。这个问题是唯一的挑战，原因有几个：首先，地形表面上的地球图像的大小通常比流行的深卷积神经网络的输入要大得多。其次，存在表面上像素类之间的拓扑结构依赖性，这种依赖性可以遵循未知和非线性分布。第三，通常有限的培训标签。现有的地球图像分割方法通常将图像分为斑块，并将其视为附加特征通道。这些方法并未完全纳入表面斑块内部和跨表面斑块内的空间拓扑结构约束，因此通常会显示出较差的结果，尤其是当训练标签受到限制时。关于地球图像的半监督和无监督学习的现有方法通常集中于学习表示，而无需明确纳入表面拓扑。相比之下，我们提出了一个新颖的框架，该框架明确地模拟了地形表面的拓扑骨架，并带有计算拓扑的轮廓树，该拓扑由物理约束（例如，地形上的水流方向）引导。我们的框架由两个神经网络组成：一个卷积神经网络（CNN），以学习2D图像网格上的空间上下文特征，以及图形神经网络（GNN），以了解物理引导的空间拓扑依赖性的统计分布。这两个模型通过变异EM共同训练。对现实世界洪水映射数据集的评估表明，所提出的模型在分类精度中的表现优于基线方法，尤其是在训练标签受到限制时。
Given earth imagery with spectral features on a terrain surface, this paper studies surface segmentation based on both explanatory features and surface topology. The problem is important in many spatial and spatiotemporal applications such as flood extent mapping in hydrology. The problem is uniquely challenging for several reasons: first, the size of earth imagery on a terrain surface is often much larger than the input of popular deep convolutional neural networks; second, there exists topological structure dependency between pixel classes on the surface, and such dependency can follow an unknown and non-linear distribution; third, there are often limited training labels. Existing methods for earth imagery segmentation often divide the imagery into patches and consider the elevation as an additional feature channel. These methods do not fully incorporate the spatial topological structural constraint within and across surface patches and thus often show poor results, especially when training labels are limited. Existing methods on semi-supervised and unsupervised learning for earth imagery often focus on learning representation without explicitly incorporating surface topology. In contrast, we propose a novel framework that explicitly models the topological skeleton of a terrain surface with a contour tree from computational topology, which is guided by the physical constraint (e.g., water flow direction on terrains). Our framework consists of two neural networks: a convolutional neural network (CNN) to learn spatial contextual features on a 2D image grid, and a graph neural network (GNN) to learn the statistical distribution of physics-guided spatial topological dependency on the contour tree. The two models are co-trained via variational EM. Evaluations on the real-world flood mapping datasets show that the proposed models outperform baseline methods in classification accuracy, especially when training labels are limited.