Segmentation of linear map objects using sequential convolutional neural network

doi:10.11947/j.AGCS.2023.20210317

Abstract

Abstract: Segmentation of linear objects based on their morphological characteristics is a pivotal pre-step for adaptive generalization. Existing studies mainly use hand-crafted features, such as length, angle, and curvature, to describe the local structures of linear objects, and further to identify different patterns based on manual-defined rules or machine learning methods. In this study, we propose a structural recognition and segmentation method for linear objects using deep learning. First, a linear unit (also known as lixel) composed of two adjacent points is considered as the processing unit, and each linear object is discretized into a two-dimension sequence in which the differences between the horizontal and vertical coordinates of each lixel are encoded. Then, a sequential convolutional neural network (SCNN) is established to predict the types of each lixel. Finally, the segmentation results of different morphological characteristics are obtained by merging the adjacent lixels with the same type using an iteration method. Experiments were conducted on two datasets of 1∶50 k administrative boundaries and 1∶250 k mountain roads, and the consistency ratios of segmentation results were 91.25% and 85.65%, respectively, outperforming the traditional methods based on backpropagation artificial neural network and Naïve Bayes. Overall, our method can effectively avoid the subjectivity that exists when designing the hand-crafted features, and is more adaptable to the segmentation of linear objects with different scales and types.

Key words: linear map objects, segmentation, sequential convolutional neural network, deep learning

CLC Number:

P208

YANG Min, CHEN Guo, LI Lianying, HUANG Haoran, MIAO Jing, YAN Xiongfeng. Segmentation of linear map objects using sequential convolutional neural network[J]. Acta Geodaetica et Cartographica Sinica, 2023, 52(1): 108-116.

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