Denoising and dimensionality reduction using multilinear tools for hyperspectral images
Denoising and dimensionality reduction using multilinear tools for hyperspectral images
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DOI：
10.1109/lgrs.2008.915736
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发表时间：
2008-04-01
影响因子：
4.8
通讯作者：
Blanc-Talon, Jacques
中科院分区：
文献类型：
--
作者：
Renard, Nadine;Bourennane, Salah;Blanc-Talon, Jacques
在高光谱图像(HSI)分析中，分类需要光谱降维。通常的DR方法使用线性代数，而我们提出了一种多线性代数方法来联合实现去噪，而DR多线性工具通过联合处理空间和光谱的方式来将HSI数据作为一个整体来考虑。将低阶(K-1，K-2，K-3)张量近似[LRTA-(K-1，K-2，K-3)]成功地应用于彩色图像等多路数据去噪。首先，我们证明了LRTA-(K-1，K-2，K-3)作为一种去噪预处理可以很好地改善分类结果。然后，我们提出了一种新的方法，称为LRTA(DR)-(K-1，K-2，D-3)，它同时执行空间低阶近似和谱DR。分类算法频谱角度映射器被应用于以下三种DR和去噪方法的输出中，以比较它们的效率：提出的LRTA(DR)-(K-1，K-2，D-3)，PCA(DR)，以及结合维纳滤波或小波变换系数软收缩的PCA(DR)。
In hyperspectral image (HSI) analysis, classification requires spectral dimensionality reduction (DR). While common DR methods use linear algebra, we propose a multilinear algebra method to jointly achieve denoising reduction and DR. Multilinear tools consider HSI data as a whole by processing jointly spatial and spectral ways. The lower rank-(K-1, K-2, K-3) tensor approximation [LRTA-(K-1, K-2, K-3)] was successfully applied to denoise multiway data such as color images. First, we demonstrate that the LRTA-(K-1, K-2, K-3) performs well as a denoising preprocessing to improve classification results. Then, we propose a novel method, referred to as LRTA(dr)-(K-1, K-2, D-3), which performs both spatial lower rank approximation and spectral DR. The classification algorithm Spectral Angle Mapper is applied to the output of the following three DR and noise reduction methods to compare their efficiency: the proposed LRTA(dr)-(K-1, K-2, D-3), PCA(dr), and PCA(dr) associated with Wiener filtering or soft shrinkage of wavelet transform coefficients.