基于小波阈值降噪和经验模态分解的高光谱图像分类算法

为了充分降低高光谱图像中的噪声以获得高精度的分类结果,本文结合小波阈值降噪(WTD)和经验模态分解(EMD)的优点,提出了一种基于小波阈值降噪-经验模态分解的高精度支持向量机(SVM)高光谱图像分类算法(WTD-EMD-SVM)。首先对高光谱图像进行小波阈值降噪,除去高光谱数据中的高频噪声;然后再对高光谱图像进行EMD,获得含有高光谱数据本质特征的内固模态函数(IMF)和含有低频噪声的残差;最后采用内固模态函数重构高光谱图像,并对高光谱图像进行SVM分类。将其应用到AVIRIS数据92AV3C,仿真结果表明该算法不仅提高了高光谱图像分类精度,同时可减少支持向量数目,以提高高光谱图像分类速度。

Hyperspectral Images Classification Based on Wavelet Threshold Denoising and Empirical Mode Decomposition

In order to remove noise and achieve high accuracy classification of hyperspectral images,a high-accuracy hyperspectral images classification algorithm based on wavelet threshold denoising(WTD) and empirical modal decomposition(EMD) is presented.First,high-frequency noise in hyperspectral images is removed by wavelet threshold denoising.Second,the essential characteristics of hyperspectral images are extracted through the decomposition of hyperspectral images with EMD,and the residual with low-frequency noise is removed.Finally,the hyperspectral images are classified with SVM,which have been composed by the Intrinsic Modal Function(IMF) of hyperspectral images.Experimental results of the AVIRIS data indicate that the proposed approach not only improves the classification accuracy of hyperspectral images,but also reduces the number of support vectors and improves the speed of hyperspectral images classification.