江西科学2025，Vol.43Issue(1)：59-64,6.DOI:10.13990/j.issn1001-3679.2025.01.008
基于自组织迁移算法小波阈值矿区遥感图像去噪研究
Study on Denoising of Mining Area Remote Sensing Images Based on Self-organizing Transfer Algorithm and A Wavelet Threshold
摘要
Abstract
Remote sensing images play an important role in environmental protection,ecological restoration,and geological monitoring in mining areas.The environmental conditions in mining areas are complex,and the presence of noise in the acquisition process of remote sensing images can seriously affect the accuracy of image land classification.To address this issue,a wavelet threshold function based on self-organizing transfer alg orithm was developed,building on the existing threshold functions.Self-organizing transfer alg orithm(SOMA)was used to screen the function parameters for multi-path optimal values.The denoisin g performance was evaluated usin gpeak signal-to-noise ratio(PSNR),mean square error(MSE),and structural similarity(SSIM)index.The results show that improving the threshold function through mathematical analysis has good continuity,and the threshold function based on self-organizing algorithm has better denoising quality for mine remote sensing images than traditional hard threshold and soft threshold functions.Additionally,the improved threshold function also further enhances the discrimination of land features in mining images.关键词
矿山遥感图像/SOMA算法/小波变换/阈值函数模型Key words
remote sensing images of mines/SOMA algorithm/wavelet transform/threshold function model分类
天文与地球科学引用本文复制引用
胡志峰,吴龙华,陈竹安..基于自组织迁移算法小波阈值矿区遥感图像去噪研究[J].江西科学,2025,43(1):59-64,6.基金项目
江西省教育厅科学技术研究项目(GJJ2206614) （GJJ2206614）
教育部人文社会科学研究项目(22YJCZH150) （22YJCZH150）
江西省社科基金"十四五"地区项目(22DQ44). （22DQ44）
