Feature space fusion classification of remote sensing image based on ant colony optimisation algorithm Online publication date: Mon, 31-Jan-2022
by Qing Sun; Quanyuan Wu
International Journal of Information and Communication Technology (IJICT), Vol. 20, No. 2, 2022
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.
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