Open AccessBuilding extraction based on improved message passing network
Author(s) -
Manlin Zhang,
Xiaoming Guo,
Ying Bao
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1961/1/012010
Subject(s) - computer science , enhanced data rates for gsm evolution , extraction (chemistry) , set (abstract data type) , data set , field (mathematics) , information extraction , artificial intelligence , data mining , pattern recognition (psychology) , mathematics , chemistry , chromatography , pure mathematics , programming language
In this paper, an improved building edge extraction method based on message passing networks (MPNNs) is proposed. In the field of high-resolution remote sensing image processing and analysis, the accuracy of building edge extraction is studied from three aspects: data set construction, network training and post-processing of edge probability map. Based on the WHU building data set, the proposed method can effectively extract the building edge, and can maintain high extraction accuracy. PPG Net, L-CNN and UNet++ algorithm is used to compare with the results of this paper. Through the analysis of experiments, the results show that the method proposed in this paper has advantages over PPG Net, L-CNN and UNet++ algorithm in the accuracy, recall and F1 score of region extraction.