Fourier descriptor-based neural network method for high-precision shape recognition of building polygon

doi:10.11947/j.AGCS.2022.20210730

Abstract

Abstract: Shape recognition is one of the important contents of map spatial cognition, and neural network combined with spatial cognitive experiment and its effective shape feature vector extraction are effective ways to improve shape recognition. In this paper, a neural network building-polygon shape recognizer is constructed, which integrates the Fourier descriptors of macro shape parameters such as roundness, eccentricity and rectangularity as shape feature vectors. Firstly, the Fourier shape descriptors, circularity, eccentricity and rectangularity parameters of building polygons are extracted by Fourier transform and computational geometry methods, and the shape feature vectors are formed. Then, the neural network recognizer matching between building polygon and shape template is realized through the training of sample data. The results show that this method greatly improves the accuracy (98.7%) compared with the previous methods, and the feature extraction algorithm is not limited by the inconsistency of polygon points. The shape recognition of real building data in Wuhan and Zhengzhou is carried out, and its information entropy is calculated. This method has good recognition effect.

Key words: Fourier shape descriptor, neural network, shape template match, building-polygon shape recognition

CLC Number:

P208

LIU Pengcheng, HUANG Xin, MA Hongran, YANG Min. Fourier descriptor-based neural network method for high-precision shape recognition of building polygon[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(9): 1969-1976.

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