针对高光谱图像存在的光谱域噪声提出了基于经验模态分解的光谱域滤波方法(emp irical mode decomposi-tion based filter(EMDF)).首先基于光谱曲线二阶导数给出了光谱曲线噪声大小的判定,然后用EMDF方法进行逐像元滤波.在容易引入空间...针对高光谱图像存在的光谱域噪声提出了基于经验模态分解的光谱域滤波方法(emp irical mode decomposi-tion based filter(EMDF)).首先基于光谱曲线二阶导数给出了光谱曲线噪声大小的判定,然后用EMDF方法进行逐像元滤波.在容易引入空间域噪声的光谱区间,以基于光谱导数的Savitzky-Golay滤波方法(derivative based savitz-ky-Golay filter(DSGF))进行替代,这样既抑制了空间域噪声的产生,也取得了较好的光谱域滤波效果.展开更多
Conventional f-x empirical mode decomposition（EMD） is an effective random noise attenuation method for use with seismic profiles mainly containing horizontal events.However,when a seismic event is not horizontal,the...Conventional f-x empirical mode decomposition（EMD） is an effective random noise attenuation method for use with seismic profiles mainly containing horizontal events.However,when a seismic event is not horizontal,the use of f-x EMD is harmful to most useful signals.Based on the framework of f-x EMD,this study proposes an improved denoising approach that retrieves lost useful signals by detecting effective signal points in a noise section using local similarity and then designing a weighting operator for retrieving signals.Compared with conventional f-x EMD,f-x predictive filtering,and f-x empirical mode decomposition predictive filtering,the new approach can preserve more useful signals and obtain a relatively cleaner denoised image.Synthetic and field data examples are shown as test performances of the proposed approach,thereby verifying the effectiveness of this method.展开更多
文摘针对高光谱图像存在的光谱域噪声提出了基于经验模态分解的光谱域滤波方法(emp irical mode decomposi-tion based filter(EMDF)).首先基于光谱曲线二阶导数给出了光谱曲线噪声大小的判定,然后用EMDF方法进行逐像元滤波.在容易引入空间域噪声的光谱区间,以基于光谱导数的Savitzky-Golay滤波方法(derivative based savitz-ky-Golay filter(DSGF))进行替代,这样既抑制了空间域噪声的产生,也取得了较好的光谱域滤波效果.
基金supported by the National Natural Science Foundation of China(No.41274137)the National Engineering Laboratory of Offshore Oil Exploration
文摘Conventional f-x empirical mode decomposition（EMD） is an effective random noise attenuation method for use with seismic profiles mainly containing horizontal events.However,when a seismic event is not horizontal,the use of f-x EMD is harmful to most useful signals.Based on the framework of f-x EMD,this study proposes an improved denoising approach that retrieves lost useful signals by detecting effective signal points in a noise section using local similarity and then designing a weighting operator for retrieving signals.Compared with conventional f-x EMD,f-x predictive filtering,and f-x empirical mode decomposition predictive filtering,the new approach can preserve more useful signals and obtain a relatively cleaner denoised image.Synthetic and field data examples are shown as test performances of the proposed approach,thereby verifying the effectiveness of this method.
