Restoration of Hyperspectral Push-Broom Scanner Data

Several effects combine to distort the multispectral data that are obtained from push-broom scanners. We develop an algorithm for restoration of such data, illustrated on images from the ROSIS scanner. In push-broom scanners variation between elements in the detector array results in a strong striping along flight lines. A non-systematic striping is also present along flight lines. Furthermore, line drop-outs occur, and finally, various types of electronic noise of salt-and-pepper type are also present. We describe techniques for the correction for all these types of effects. Line drop-outs are located automatically using line means statistics, and if present new pixel values are interpolated from the neighbouring lines. Striping along and across flight lines is corrected for by adjusting the line and column means, respectively. This restoration is carried out in stationary parts of the image, for instance over water.Following these initial corrections we use minimum/maximum autocorrelation factor (MAF) analysis in order to separate the spatially coherent signal components from the noise components. The MAF transformation is a linear transformation into new orthogonal variables that are ordered by decreasing autocorrelation. In this way noise channels (with low autocorrelation) can be identified and cleaned or eliminated. Also, the MAF transformation enables us to isolate electronic or aircraft engine induced noise components that have a special spatial structure. Subsequent inverse transformation back into the original spectral space results in noise corrected variables. The noise components will now have been removed from the entire original data set by working on a smaller set of noise contaminated transformed variables only. The application of the above techniques results in a dramatic increase in visual image quality.