Filtering high-resolution hyperspectral imagery in a maximum noise fraction transform domain using wavelet-based de-striping

Ever improving technology and computer processing power and decreasing cost have made hyperspectral image acquisition and analysis affordable in many applications. Hyperspectral images, acquired normally using pushbroom sensing systems, are tainted with noise and nonperiodic stripes. Few methods, including wavelet-based ones, have been proposed for reducing nonperiodic stripes from multispectral images; there are even fewer studies dealing with nonperiodic stripes in high-resolution hyperspectral images. Applying de-striping filters directly to individual hyperspectral image bands can be computationally inefficient and complicated considering the large number of bands in this type of image. This article compares the performance of wavelet-based de-striping algorithms as applied on high-resolution hyperspectral imagery. The algorithms are implemented directly on individual bands in the image domain and on selected bands in the image maximum noise fraction (MNF) transform domain. Two wavelet-based de-striping algorithms were tested and compared. The first algorithm eliminates wavelet detail components in the striping direction, while the second algorithm adaptively filters these components. The filtering methods are evaluated through visual and quantitative assessments. Quantitative assessment is performed by analysing the autocorrelation coefficient and signal-to-noise-ratio. The results show that images filtered in the MNF domain are superior in reducing stripes and noise while retaining the image information and without introducing distortions. The technique is computationally effective through filtering fewer bands, which reduces the need for filtering parameter determination and fine tuning. Visual and quantitative assessments also show that adaptive filtering of wavelet components is better than eliminating entire components due to the retention of image content.