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Received：23 July 2023，
Published：07 July 2024
移动端阅览
近年来突发性地表异常ESA（Earth Surface Anomalies）事件频发且呈上升趋势，给人类的生命、财产安全带来了巨大威胁，如何及时准确地发现地表异常事件对后续救援与灾害响应具有重要意义。一些研究人员利用卫星影像开展大尺度地表异常发现与监测，并开发、运用了先进的深度学习方法。然而，由于标签数据不足和卫星影像中几何、语义信息十分复杂，现阶段的地表异常检测方法的表现往往不能达到很好的效果。针对上述问题，本文提出了一个多层级几何—语义融合的图神经网络GNN（Graph Neural Network）框架，以实现高精度地表异常快速发现。具体而言，本文提出的方法先利用两个不同的分支从输入的卫星影像中提取不同层级的地理实体并构建图表示，然后通过分配矩阵实现图的联合表达。此后，构建了分层图注意力网络GAT（Graph Attention Network），基于图节点信息传递、聚合与注意力机制从图中进一步挖掘卫星影像中复杂的几何、语义信息，用于准确地检测地表异常。考虑到现有大规模多类地表异常检测基准数据集的缺乏，我们基于现有公开可分发数据集构建了ESAD数据集，用于基于单张卫星影像的地表异常检测任务。大量实验表明，提出的方法在地表异常检测任务中取得了优异的性能，在准确率、召回率与推理时间方面优于许多基线方法，可精确、有效地开展地表异常检测任务。
The increasing occurrence of Earth Surface Anomalies (ESAs) highlights the importance of the timely and accurate detection of such events. Therefore
researchers have utilized satellite imagery for large-scale detection and developed advanced deep learning methods. However
the performance of the methods is hindered by inadequate labeled data and the complexity of semantic information in satellite imagery. In this paper
we aim to address the aforementioned problems and improve the performance of Earth surface anomaly detection. This paper reviews and summarizes developments in Earth’s surface anomaly detection and the problems that hinder the performance of existing methods. Then
we proposed a hierarchical geometry-to-semantic fusion Graph Neural Network (GNN) framework
which utilizes a single image for the detection of Earth’s surface anomalies and reduces the demand for data and time required for preprocessing and inference. Specifically
our method employs two branches for the extraction of geoentities and construction of graphs at different levels. Then
a hierarchical graph attention network was utilized to update node features and extract graph embeddings for each level. An attention-based feature fusion module then combined them to yield the graph-level feature vector of the input image
which was finally processed through a multilayer perceptron for ESA detection. Given that existing ESA datasets mainly focus on single-class detection or post-hoc analysis
which is insufficient for our research needs
we proposed ESAD
which is a composite dataset
to bridge the gap between large-scale multiclass data
sets. Specifically
ESAD is composed of three publicly datasets: xBD
Multi
3
Net and Sichuan Landslide
and Debrisflow. The proposed method was effective and accurate for detecting ESA
outperforming many baseline methods and balancing between accuracy and efficiency. Thus
it is suitable for ESA detection
saving valuable time and resources for downstream tasks. In conclusion
we proposed a hierarchical geometry-to-semantic fusion GNN framework for ESA detection. It leverages GNN to learn high-order semantic information from satellite imagery. To address insufficiency in benchmark datasets
we created the ESAD dataset based on existing related datasets. Our method achieved a good balance between accuracy and efficiency and is suitable for ESA detection with high timeliness requirements. In future work
we will further explore more models and extend our method to on-orbit real-time ESA detection task.
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