利用语义网格从多视角航空影像中分类地理空间对象
Classifying geospatial objects from multiview aerial imagery using semantic meshes
AI总结：
针对正射镶嵌图输入缺失垂直信息、存在伪影的问题，提出基于语义网格的多视角航空影像地理空间对象分类方法并开源，在跨站点树种分类任务上将准确率从53%提升至75%。
摘要：
航空影像正越来越多地应用于地球科学与自然资源管理领域，作为对劳动密集型地面调查的补充。航空系统可采集重叠影像，从不同视角提供每个地点的多视图数据。然而，大多数预测方法（例如用于树种分类的方法）都将单张合成的俯视“正射镶嵌图”作为输入，这类图像几乎不包含对象的垂直维度信息，还可能存在处理伪影。我们提出了一种替代方法，可直接在原始影像上生成预测结果，并借助语义网格（semantic meshes）将这些预测结果精准映射至地理空间坐标。该方法已作为一款用户友好的开源工具包发布，它让分析人员能够使用最高质量的数据开展预测，捕捉对象侧面的信息，并利用每个地点的多视角数据提升鲁棒性。我们在美国西部4个森林站点构建的全新基准数据集上验证了该方法的价值，该数据集包含无人机影像、摄影测量结果、预测的树木位置，以及来自人工调查的树种分类数据。实验表明，在一项具有挑战性的跨站点树种分类任务中，我们提出的多视角方法相较于正射镶嵌图基线，将分类准确率从53%提升至75%。
英文摘要：
Aerial imagery is increasingly used in Earth science and natural resource management as a complement to labor-intensive ground-based surveys. Aerial systems can collect overlapping images that provide multiple views of each location from different perspectives. However, most prediction approaches (e.g. for tree species classification) use a single, synthesized top-down "orthomosaic" image as input that contains little to no information about the vertical aspects of objects and may include processing artifacts. We propose an alternate approach that generates predictions directly on the raw images and accurately maps these predictions into geospatial coordinates using semantic meshes. This method$\unicode{x2013}$released as a user-friendly open-source toolkit$\unicode{x2013}$enables analysts to use the highest quality data for predictions, capture information about the sides of objects, and leverage multiple viewpoints of each location for added robustness. We demonstrate the value of this approach on a new benchmark dataset of four forest sites in the western U.S. that consists of drone images, photogrammetry results, predicted tree locations, and species classification data derived from manual surveys. We show that our proposed multiview method improves classification accuracy from 53% to 75% relative to an orthomosaic baseline on a challenging cross-site tree species classification task.
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