基于改进卷积神经网络的山顶点识别研究
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西安建筑科技大学信息与控制工程学院

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陕西省自然科学(2019JM-183);地理信息工程国家重点实验室开放基金(SKLGIE2018-Z-4-1);国家重点研发计划项目(2019YFD1100901)


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    摘要:

    针对传统山顶点识别方法中特征选择困难等问题,借助深度卷积神经网络特征自学习的优势,将格网DEM数据中的山顶点提取转换为数字图像中的目标检测问题,提出一种基于改进Faster R-CNN的山顶点识别方法。将DEM数据处理为等高线图与灰度图叠加的形式,采用基于Faster R-CNN的目标识别框架,以ResNet-101替代原始的VGG16作为山顶识别模型的特征提取网络,并在RPN锚框尺寸设置中引入K-Means聚类算法,实现适用于自建山顶样本集PEAK-100的锚框参数设定。利用改进后的Faster R-CNN自动提取山顶的深度特征,生成高质量的山顶区域,并结合高程标识出最终的山顶点坐标。实验结果表明,新方法的山顶点识别准确率为94.82%,相比于传统方法漏提率减少约60%,在一定程度上避免了山顶识别效果易受人工选择特征的影响。

    Abstract:

    This paper aims to solve the problem of difficulty in feature selection of mountain peak, considering the transformation of mountain peak recognition in DEM data into a target detection task in digital images. With the help of deep learning technology, a mountain peak extraction method based on improved Faster R-CNN is designed. First, the DEM data is superimposed on the contour map and the grayscale map, Used the Faster R-CNN-based target recognition framework, replaced the original VGG16 with ResNet-101 as the feature extraction network of the mountain peak recognition model, and introduced the K-Means clustering algorithm in the RPN anchor frame size setting to realize the application of self-built mountain peaks the anchor frame parameter setting of the sample set PEAK-100. The improved Faster R-CNN is used to automatically extract the depth features of the mountain top,generate high-quality mountain top area, and combine the elevation to identify the final mountain peak coordinates.The experimental results show that the accuracy of the new method of mountain apex extraction is 94.82%, Compared with the traditional method, the omission rate is reduced by about 60%. To a certain extent, it reduces the mountain peak extraction effect that is susceptible to manual selection of features, and provide a new point element recognition in DEM technical approach.

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李凯明,孔月萍,张跃鹏,朱旭东,高凯.基于改进卷积神经网络的山顶点识别研究计算机测量与控制[J].,2021,29(11):154-158.

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  • 收稿日期:2021-03-30
  • 最后修改日期:2021-04-28
  • 录用日期:2021-04-28
  • 在线发布日期: 2021-11-22
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