摘要
利用超平面最小方案,针对高光谱数据在空间维和光谱维建立能量函数,通过两个权重系数调节空间维数据曲面光滑程度和光谱曲线光滑程度,达到联合抑制噪声的目的。实验中,对Hamamatsu相机和AVIRIS采集的高光谱影像数据中比较严重的噪声污染,该方法有效地降低了噪声的影响,在AVIRIS水吸收带处的去噪效果尤为明显。
In order to denoise in spatial and spectral dimensions at the same time,a denoising method based on energy variations is provided for hyperspectral image.With the hypersurface minimal scheme,energy functional is built of spatial surface area,spectral curve length and residual l2 norm.The minimal surface area term smoothes data in spatial dimensions and the minimal curve length term works in spectral dimension whose weights are determined by two positive coefficients.The surface mean curvature and curve curvature drive suppressing of noise together.Then perturbation is abated by available neighboring reliable data.The proposed method works well in experiments on hyperspectral image from Hamamatsu camera and AVIRIS.
出处
《武汉大学学报（信息科学版）》
EI
CSCD
北大核心
2012年第3期322-325,共4页
Geomatics and Information Science of Wuhan University
基金
湖北省自然科学基金资助项目(2011CDB4521)
湖北省自然科学基金重点资助项目(2009CDA141)
国家科技支撑计划资助项目(2011BAK08B03)
中央高校基本科研业务费专项资金资助项目(111078)
国家自然科学基金面上资助项目(61070079)
关键词
能量变分
超平面最小
高光谱影像
去噪
energy variations
hypersurface minimal
hyperspectral image
denoising
