Title: Feature space fusion classification of remote sensing image based on ant colony optimisation algorithm

Authors: Qing Sun; Quanyuan Wu

Addresses: College of Geography and Environment, Shandong Normal University, Ji'nan, 250014,China; College of Finance, Qilu University of Technology (Shangdong Academy of Sciences), Ji'nan,250000,China ' College of Geography and Environment, Shandong Normal University, Jinan, 250014,China

Abstract: In order to overcome the problems of low classification accuracy and poor application effect of traditional remote sensing image feature space fusion classification method, a new remote sensing image feature space fusion classification method based on ant colony optimisation algorithm is proposed. According to the ant colony algorithm state transition rule, the global optimal path is updated. The spatial structure, edge and texture features of remote sensing image are extracted by feature extractor. The fusion weight coefficient of remote sensing image space and spectral feature vector is calculated. The extracted remote sensing image feature vector is replaced by the maximum likelihood method Image classification discriminant formula is used to realise remote sensing image feature space fusion classification. The experimental results show that the average classification accuracy is improved by 9.75%, and the classification speed is improved by 15.6%, which effectively improves the image recognition rate.

Keywords: ant colony optimisation algorithm; remote sensing image; image feature; feature space fusion; image classification.

DOI: 10.1504/IJICT.2022.120634

International Journal of Information and Communication Technology, 2022 Vol.20 No.2, pp.164 - 176

Received: 20 May 2020
Accepted: 06 Aug 2020
Published online: 31 Jan 2022 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article