矿区遥感图像去噪方法研究

去噪是矿区遥感图像得以有效应用的重要预处理步骤。现有的基于统计、基于域变换、基于学习等遥感图像去噪方法普遍存在细节过度平滑、纹理保持不足等问题。基于引导滤波良好的边缘保持特性,提出了迭代引导滤波方法,通过对残差信息进行引导映射,并迭代进行引导滤波及超参数收缩,增强了遥感图像边缘特征提取效果;将迭代引导滤波与传统的小波软阈值、非局部均值(NLM)滤波、三维块匹配(BM3D)滤波等去噪方法结合,有效提高了传统方法的峰值信噪比,其中NLM滤波、BM3D滤波的去噪性能提升效果最明显;将迭代引导滤波与BM3D滤波融合,通过BM3D滤波初步获取去噪图像,得到残差数据,然后采用迭代引导滤波对残差数据进行处理,在提升图像去噪效果的同时,很好地保持了图像细节特征;将迭代引导滤波与BM3D滤波融合方法用于矿区遥感图像的煤矸石场识别及滑坡区域边缘识别,取得了较好的效果。

Research on denoising method of remote sensing image in mining area

Denoising is an important preprocessing step for the effective application of remote sensing images in mining area.The existing remote sensing image denoising methods based on statistics,domain transformation and learning generally have the problems of excessive smoothing of details and insufficient texture preservation.Based on the good edge-preserving property of guided filtering,an iterative guided filtering method is proposed.The method enhances the edge characteristics extraction effect of remote sensing images by guided mapping of residual information,and iteratively performing guided filtering and hyper-parameter shrinkage.The iterative guided filtering is combined with traditional wavelet soft threshold,non-local mean(NLM)filtering,block matching 3D(BM3D)filtering and other denoising methods,which improves the peak signal-to-noise ratio of the traditional method effectively.Among them,NLM and BM3D filtering have the most obvious effects on improving the denoising performance.The iterative guided filtering and BM3D filtering are fused,and the denoised images are initially obtained through BM3D filtering to obtain residual data.The iterative guided filtering is used to process the residual data.While improving the image denoising effect,the image detail characteristics are well preserved.The iterative guided filtering and BM3D filtering fusion method are used for coal gangue yard identification and landslide area edge recognition in remote sensing images of mining areas,and good results have been achieved.