基于深度卷积神经网络的扫描地形图点符号识别

Wenjun Huang, Qun Sun, Anzhu Yu, Wenyue Guo, Qing Xu, Bowei Wen, Li Xu
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引用次数: 1

摘要

扫描地形图(STM)上的点符号提供重要的地理信息。然而,由于图元的粘性和符号结构的奇异性,点符号识别具有较高的复杂性和不确定性。因此,从stm中提取点符号是一个挑战。目前,点符号识别主要是通过模式识别方法进行的,这种方法的准确率和效率都不高。为了解决这个问题,我们研究了基于深度学习的点符号识别方法的潜力,并提出了一个基于深度卷积神经网络(DCNN)的模型。我们创建了来自不同来源的点符号数据集,用于训练和预测模型。在此框架下,采用自然空间金字塔池(ASPP)来解决点符号与自然物体之间的差异所带来的识别困难。为了提高定位精度,采用k-means++聚类方法生成更适合我们点符号数据集的锚盒。此外,为了提高模型的泛化能力,我们设计了两种适应符号识别的数据增强方法。实验表明,与经典算法相比,深度学习方法显著提高了识别精度和效率。在目标检测算法中引入ASPP,使得平均精度和交联值更高,表明识别精度更高。结果表明,数据增强方法可以有效地缓解跨域问题,提高旋转鲁棒性。该研究有助于算法的发展和对从stm中提取的地理要素的评估。
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Leveraging Deep Convolutional Neural Network for Point Symbol Recognition in Scanned Topographic Maps
Point symbols on a scanned topographic map (STM) provide crucial geographic information. However, point symbol recognition entails high complexity and uncertainty owing to the stickiness of map elements and singularity of symbol structures. Therefore, extracting point symbols from STMs is challenging. Currently, point symbol recognition is performed primarily through pattern recognition methods that have low accuracy and efficiency. To address this problem, we investigated the potential of a deep learning-based method for point symbol recognition and proposed a deep convolutional neural network (DCNN)-based model for this task. We created point symbol datasets from different sources for training and prediction models. Within this framework, atrous spatial pyramid pooling (ASPP) was adopted to handle the recognition difficulty owing to the differences between point symbols and natural objects. To increase the positioning accuracy, the k-means++ clustering method was used to generate anchor boxes that were more suitable for our point symbol datasets. Additionally, to improve the generalization ability of the model, we designed two data augmentation methods to adapt to symbol recognition. Experiments demonstrated that the deep learning method considerably improved the recognition accuracy and efficiency compared with classical algorithms. The introduction of ASPP in the object detection algorithm resulted in higher mean average precision and intersection over union values, indicating a higher recognition accuracy. It is also demonstrated that data augmentation methods can alleviate the cross-domain problem and improve the rotation robustness. This study contributes to the development of algorithms and the evaluation of geographic elements extracted from STMs.
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