论文标题
上下文金字塔注意网络，用于建立空中图像中的分割
Contextual Pyramid Attention Network for Building Segmentation in Aerial Imagery
论文作者
论文摘要
从空中图像中提取提取，在城市规划，变更检测和灾难管理等问题中有多种应用。随着数据可用性的增加，近年来，用于遥感图像的语义分割的卷积神经网络（CNN）已大大改善。但是，卷积在当地社区运作，无法捕获对空中图像的语义理解至关重要的非本地特征。在这项工作中，我们建议通过使用上下文金字塔注意（CPA）捕获长期依赖性来改善不同大小的建筑细分。该路径有效地处理多个尺度的输入，并以加权方式组合，类似于集成模型。提出的方法在以最低的计算成本上获得了Inria空中图像标签数据集的最先进性能。我们的方法比当前的最新方法提高了1.8点，比联合（IOU）公制的交叉点上的现有基线高12.6点，而无需任何后处理。代码和模型将公开可用。
Building extraction from aerial images has several applications in problems such as urban planning, change detection, and disaster management. With the increasing availability of data, Convolutional Neural Networks (CNNs) for semantic segmentation of remote sensing imagery has improved significantly in recent years. However, convolutions operate in local neighborhoods and fail to capture non-local features that are essential in semantic understanding of aerial images. In this work, we propose to improve building segmentation of different sizes by capturing long-range dependencies using contextual pyramid attention (CPA). The pathways process the input at multiple scales efficiently and combine them in a weighted manner, similar to an ensemble model. The proposed method obtains state-of-the-art performance on the Inria Aerial Image Labelling Dataset with minimal computation costs. Our method improves 1.8 points over current state-of-the-art methods and 12.6 points higher than existing baselines on the Intersection over Union (IoU) metric without any post-processing. Code and models will be made publicly available.
