本文借鉴具有自适应特性的一维局域均值分解算法(Local Mean Decomposition, BLMD),提出了二维局域均值分解算法(Bidimensional Local Mean Decomposition, BLMD).二维局域均值分解算法可以将源图像分解成多个二维生产函数分量(Bidimens...本文借鉴具有自适应特性的一维局域均值分解算法(Local Mean Decomposition, BLMD),提出了二维局域均值分解算法(Bidimensional Local Mean Decomposition, BLMD).二维局域均值分解算法可以将源图像分解成多个二维生产函数分量(Bidimensional Product Function, BPF).思路为:先通过可变邻域窗法来获得分解过程中的极值点,而后利用分形理论对图像进行插值操作,并得到相应的均值曲面的等信息,再对筛分过程中相邻曲面之间在零值平面投影上不重合极值点数目进行统计和分析,给出符合图像本身特性的停止条件,保证分解得到的BPF分量能够真实反映图像的某类特征信息.最后,在此基础上形成本文提出的二维局域均值分解算法.通过实证分析表明,本方法可以自适应对图像进行分解.展开更多
As a powerful tool for image processing,bi-dimensional empirical mode decomposition (BEMD) covers a wide range of applications. In this paper,we explore a novel hyperspectral classification algorithm which integrates ...As a powerful tool for image processing,bi-dimensional empirical mode decomposition (BEMD) covers a wide range of applications. In this paper,we explore a novel hyperspectral classification algorithm which integrates BEMD and support vector machine (SVM) . By virtue of BEMD,the selected hyperspectral bands are decomposed into several bi-dimensional intrinsic mode functions (BIMFs) ,which reflect the essential properties of hyperspectral image. We further make full use of SVM,which is a supervised classification tool widely accepted,to classify the suitable sum of BIMFs. Experimental results indicate that though the proposed method has no advantage in computing time,it exhibits higher classification accuracy and stability than the classical SVM.展开更多
文摘本文借鉴具有自适应特性的一维局域均值分解算法(Local Mean Decomposition, BLMD),提出了二维局域均值分解算法(Bidimensional Local Mean Decomposition, BLMD).二维局域均值分解算法可以将源图像分解成多个二维生产函数分量(Bidimensional Product Function, BPF).思路为:先通过可变邻域窗法来获得分解过程中的极值点,而后利用分形理论对图像进行插值操作,并得到相应的均值曲面的等信息,再对筛分过程中相邻曲面之间在零值平面投影上不重合极值点数目进行统计和分析,给出符合图像本身特性的停止条件,保证分解得到的BPF分量能够真实反映图像的某类特征信息.最后,在此基础上形成本文提出的二维局域均值分解算法.通过实证分析表明,本方法可以自适应对图像进行分解.
基金Sponsored by the National Natural Science Foundations of China (Grant No.60975009 and 61171197)Research Fund for the Doctoral Program of Higher Education of China (Grant No.20092302110037 and 20102302110033)
文摘As a powerful tool for image processing,bi-dimensional empirical mode decomposition (BEMD) covers a wide range of applications. In this paper,we explore a novel hyperspectral classification algorithm which integrates BEMD and support vector machine (SVM) . By virtue of BEMD,the selected hyperspectral bands are decomposed into several bi-dimensional intrinsic mode functions (BIMFs) ,which reflect the essential properties of hyperspectral image. We further make full use of SVM,which is a supervised classification tool widely accepted,to classify the suitable sum of BIMFs. Experimental results indicate that though the proposed method has no advantage in computing time,it exhibits higher classification accuracy and stability than the classical SVM.
