Extraction Method of Road Network Based on Adaptive Clustering Learning

doi:10.13474/j.cnki.11-2246.2018.0345

Abstract

Abstract:

Aiming at the problem of over fitting of classifiers caused by the heterogeneity of road samples in complex scenes,this paper proposes an automatic road-network extraction method based on adaptive clustering learning.Method takes high resolution remote sensing image and older road-network as input data.Positive and negative samples are automatically acquired by the guiding of road-network.Then authors present an adaptive clustering method for road samples,which is based on the feature distribution in sample-set.Road extraction results are integrated by the majority voting method.The experimental results based on large scene data show that the proposed method can effectively take into account the different features of road objects.Quantitative experimental comparison of the results further shows the applicability of the method.

Key words: road extraction, clustering, remote sensing image, navigation road network, SVM, object-oriented

CLC Number:

P237

CHEN Guang, XUE Mei, CHEN Liangchao, SUI Haigang. Extraction Method of Road Network Based on Adaptive Clustering Learning[J]. 测绘通报, 2018, 0(11): 30-35.

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