Title: Spatial texture feature classification algorithm for high resolution 3D images

Authors: Ping Wang

Addresses: Information Engineering College of Yango University, Fujian University Engineering Research Center of Spatital Date Mining and Application, Fuzhou, 350000, China

Abstract: The existing feature classification algorithms have a lot of noise in the process of classification, which leads to the problems of low classification efficiency and unbalanced classification of image spatial texture features. Based on this, a texture feature classification algorithm based on RUSBoost is proposed. Wavelet coefficients, threshold processing and image reconstruction are used to denoise the image. On the basis of BDAWPSO algorithm, image segmentation is carried out by searching for the optimal threshold. Gabor transform and windowing are used to overcome the lack of local analysis ability and reduce the classification time. The original unbalanced image data is converted into new balanced data by using Rus boost algorithm. The experimental results show that the algorithm can improve the classification effect and display the texture information of the image better.

Keywords: high resolution; 3D image; spatial texture feature; classification algorithm.

DOI: 10.1504/IJICT.2022.125540

International Journal of Information and Communication Technology, 2022 Vol.21 No.3, pp.229 - 240

Received: 15 Oct 2020
Accepted: 11 Nov 2020
Published online: 14 Sep 2022 *

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