采用张量子空间的高光谱影像多维滤波算法

本文提出了一种基于张量子空间的多维滤波算法，将其应用于高光谱遥感影像降噪。该方法将高光谱影像数据视为三阶张量，引入张量数据表达，通过张量子空间分解将含噪影像投影到信号子空间，根据影像信号与噪声在子空间中分布的不同滤除噪声并保留原始影像的信号成分。利用该算法作用于多组含噪高光谱数据，对比逐波段二维维纳滤波算法、小波降噪算法等传统数字图像降噪算法的结果，实验证明了这种新型降噪算法的有效性。

Multi-Dimensional Filtering Algorithm for Hyperspectral Images Based on Tensor Subspace

In this paper, we propose a novel algorithm for hyperspectral image (HSI) denoising which is based on the tensor subspace. Considering the HSI as 3 order tensor data, our approach introduce a data representation involving multidimensional processing and project such data into the signal subspace by tensor subspace decomposition. The optimization criterion used in this algorithm is the minimization of mean square error between the estimated signal and the desired signal, then the alternating projection algorithm is adopted to determine the optimal filter in each dimension. Comparative studies with conventional denoising methods such as 2-D Wiener filtering and channel-by-channel wavelet thresholding show that our algorithm provides better performance using AVIRIS and PHI datasets.