标题: Pattern Recognition and Segmentation of Administrative Boundaries Using a One-Dimensional Convolutional Neural Network and Grid Shape Context Descriptor

作者: Yang, M (Yang, Min); Huang, HR (Huang, Haoran); Zhang, YQ (Zhang, Yiqi); Yan, XF (Yan, Xiongfeng)

来源出版物: ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION卷: 11期: 9文献号: 461 DOI: 10.3390/ijgi11090461出版年: SEP 2022

摘要: Recognizing morphological patterns in lines and segmenting them into homogeneous segments is critical for line generalization and other applications. Due to the excessive dependence on handcrafted features in existing methods and their insufficient consideration of contextual information, we propose a novel pattern recognition and segmentation method for lines, based on deep learning and shape context descriptors. In this method, a line is divided into a series of consecutive linear units of equal length, termed lixels. A grid shape context descriptor (GSCD) was designed to extract the contextual features for each lixel. A one-dimensional convolutional neural network (1D-U-Net) was constructed to classify the pattern type of each lixel, and adjacent lixels with the same pattern types were fused to obtain segmentation results. The proposed method was applied to administrative boundaries, which were segmented into components with three different patterns. The experiments showed that the lixel classification accuracy of the 1D-U-Net reached 90.42%. The consistency ratio was 92.41%, when compared with the manual segmentation results, which was higher than either of the two existing machine learning-based segmentation methods.

作者关键词: line segmentation; pattern recognition; one-dimensional convolutional neural network; grid shape context descriptor

地址: [Yang, Min; Huang, Haoran; Zhang, Yiqi] Wuhan Univ, Sch Resource & Environm Sci, 129 Luoyu Rd, Wuhan 430079, Peoples R China.

[Yan, Xiongfeng] Tongji Univ, Coll Surveying & Geoinformat, 1239 Siping Rd, Shanghai 200092, Peoples R China.

通讯作者地址: Yan, XF (通讯作者)，Tongji Univ, Coll Surveying & Geoinformat, 1239 Siping Rd, Shanghai 200092, Peoples R China.

电子邮件地址:xiongfengyan@tongji.edu.cn

影响因子：3.099