Modular transfer function compensation for hyperspectral data from Resurs-P satellite system

Resurs-P satellite system is one of the recent Earth remote sensing systems deployed by Russia. Its payload consists of the high resolution multispectral imager, the average resolution imager with wide swath and the hyperspectral imaging system. Hyperspectral system consists of two imagers each registering radiation in roughly half of instruments spectral range. So the output from the hyperspectral system are two hyperspectral images representing same area of the Earth but in different spectral ranges with a slight spectral overlap. For further explanation purposes these two images are named as image 'A' and image 'B'. During the on-ground processing stage images 'A' and 'B' are combined into a single hyperspectral image, covering whole instrument spectral range. During evaluation of quality of hyperspectral data it was found that modular transfer function (MTF) obtained from images 'A' and 'B' is different, resulting in better spatial resolution of image 'A' compared to 'B'. This fact could pose problems in the following analysis of hyperspectral data as the obtained spectral signatures actually represent slightly different parts of the ground in two halves of an instrument spectral range. The present work describes an algorithm of MTF compensation which purpose is to mitigate difference in spatial resolution of the data, obtained from the hyperspectral imaging system of Resurs-P satellite. The proposed algorithm is based on spatial linear filtering and is applied on the data that was previously transformed to spectral radiances and spatially co-registered. The algorithm consists of two steps. On the first step the coefficients of correction linear filter defined as a window kernel are estimated. For filter estimation we choose one spectral band from image 'A' as a reference image with the 'best' MTF and one spectral band from image 'B'. We select spectral bands from within spectral overlap range of images 'A' and 'B' so they have same spectral ranges. Then linear filter coefficients are calculated using the least square errors method, so that when applying calculated filter to image 'B' an image that is closest to 'A' is obtained. On the second step correction filter is applied to all bands in image 'B' to compensate its difference in MTF compared to image 'A'. Based on the selection of reference image it is possible to estimate the correction filter that blurs higher resolution image to lower resolution (which also reduces noise) or vice versa, i.e. the filter that increases resolution (but at the cost of increased noise). Effectiveness of the proposed algorithm is evaluated on the images obtained from Resurs-P satellites. The relative difference of resolutions of 'A' and 'B' images is reduced by more than 3 times.