Title: Study on optimal urban land classification method based on remote sensing images

Authors: Lede Niu; Mei Pan; Yan Zhou; Liran Xiong

Addresses: School of Tourism and Geographical Sciences, Yunnan Normal University, Kunming 650500, China ' School of Tourism and Geographical Sciences, Yunnan Normal University, Kunming 650500, China ' School of Tourism and Geographical Sciences, Yunnan Normal University, Kunming 650500, China ' Research Center for the Opening of Southwest China and Frontier Security, Yunnan Normal University, Kunming 650500, China

Abstract: Traditional urban land classification methods are relatively complicated in information collection, resulting in large time consumption and low classification accuracy. Therefore, an optimal urban land classification method based on remote sensing images is proposed in this paper. In this paper, four influencing factors of urban land classification are clarified based on the actual situation, on which the information about urban land in urban areas is extracted through the NDBI index method, and the ETM remote sensing image information of urban land is obtained. The genetic algorithm based on K-means mutation operator is adopted to classify the ETM remote sensing image information of urban land and finally the optimal classification results of urban land are obtained. In order to test the superiority of the method, a simulation comparison experiment was carried out. The simulation results show that the proposed method can provide overall accuracy of 93.75% and classification accuracy of 96.18% in urban land information collection, has the overall Kappa coefficient of 0.8967 and cost 10 s averagely, indicating that this method is superior in time consumption during urban information collection, collection accuracy and land classification accuracy, so it has high application value.

Keywords: remote sensing image; urban land; modelling; K-means method; NDBI index.

DOI: 10.1504/IJICT.2020.107589

International Journal of Information and Communication Technology, 2020 Vol.16 No.4, pp.365 - 377

Received: 26 Apr 2019
Accepted: 11 Jun 2019

Published online: 30 Mar 2020 *

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