A multivariate wavelet-PCA denoising-filter for hyperspectral images

In this paper we investigate the use of multivariate multiresolution principal component analysis for filtering and denoising of signals. From the proposed model we deduce several properties that particularly address the properties of hyper-spectral image data. We thereby aim at overcoming shortcomings of other methods close to the approach specifically for hyperspectral applications. The performance is evaluated by generating synthetic pure and noised signals from a physical model for spectral reflectance images. From benchmark experiments we deduce that the performance of the proposed method is equal or higher compared to univariate multiresolution denoising algorithms, while being less computationally complex. The described algorithm is used for processing of large close-range outdoor data sets of sensed crop plants.