Automatic destriping of Hyperion imagery based on spectral moment matching

Abstract

As a pushbroom imaging spectrometer, the Hyperion sensor records each along-track image column with a single detector element in one of its area detector arrays. This image acquisition configuration is prone to exhibiting along-track striping artefacts, if the instrument has not undergone a recent, proper uniformity calibration. These defective columns must be corrected before performing further processing and quantitative analyses. Spatial moment matching (SpaMM) is currently the most commonly used method for destriping Hyperion imagery. However, since SpaMM was originally developed for dealing with remotely sensed multispectral data, the abundant spectral information embedded in hyperspectral data is not fully utilized in the method. This paper proposes a new methodology that can automatically and more accurately remove the striping artefacts from Hyperion imagery. The technique is called spectral moment matching (SpcMM) because it uses spectral autocorrelation instead of spatial autocorrelation to estimate the expected mean and standard deviation of a subscene, which is comprised of the image pixels acquired by an identical detector element (an along-track image column in the case of Hyperion data). The basis of the algorithm is the observation that there are usually highly correlated groups of bands in a hyperspectral image cube; the statistics of the subscenes measured by the corresponding detectors in a set of highly correlated bands are usually very similar. The possibility of introducing undesired side effects into the destriped images by the proposed SpcMM is minimized due to the proper estimation of the expected mean and standard deviation for each along-track column. Moreover, SpcMM can automatically destripe an entire Hyperion image cube without the manual selection of defective bands or across-track spatial regions, or the individual selection of band-specific window sizes for spatial smoothing. Two Hyperion datasets acquired over sites in Seal Harbour, Canada, and Coleambally, Australia, have been used to assess the proposed new destriping technique. Examination of the images destriped by SpcMM and SpaMM and the reflectance spectra retrieved from the destriped image cubes reveals that the presented algorithm removes various types of stripes without degrading the images and is superior to the spatial moment matching technique.