Quality assessment of pan-sharpening methods

Abstract

In current technological development in electronics era have promoted real time remote sensing system to collect fine image resolution over the ground. The earth observation satellite such as Quick-bird, IKONOS, landsat etm+, CALIPSO uses the on-board optical imaging sensor while designing these sensors there are some limitation and in order to meet these limitations trade-off between the spatial resolution spectral resolutions and signal to noise ratio is made. Because of this reason optical system provides the data which consists of low resolution multispectral image and high resolution panchromatic image. High resolution colour image can be obtained from these available images by the process called as pan sharpening. This process combines low resolution multispectral image with high resolution PAN image to produce the resulting image which corresponds to multispectral images with same resolution as that of PAN image. This method provides effective way to obtain colour image with high spatial resolution for the better visual image from remote sensing system. In this paper the review of different pan sharpening methods is taken.

Joint Quality Measure for Evaluation of Pansharpening Accuracy

“…Due to the missing reference in pansharpening quality assessment task different solutions or so-called protocols were proposed: Wald's protocol [7], Zhou's protocol [8], Quality with No Reference (QNR) [9] and Khan's protocol [10], which usually include the calculation of several quality measures. Of course, a sole or joint quality measure, as already proposed in [9,11,12], enables much easier and practical/comfortable ranking of various fusion methods.…”

“…For example, the quality measure UIQI/SSIM [13] was designed for perceptual tasks or scale invariant applications, but recently it is spreading widely in other applications. Thus, its usage in pansharpening quality assessment in remote sensing imagery, e.g., QNR [9] and joint quality measures [11,12], can lead to wrong results. Because MSE and UIQI/SSIM based measures are not very suitable for translation invariant with respect to sample means and standard deviations applications [14], I propose to exchange/replace the above-mentioned UIQI/SSIM measures with a new measure-composite measure-based on means, standard deviations and correlation coefficient (CMSC) [14], which is translation invariant with respect to means and standard deviations, thus enhancing measures proposed in [12].…”

“…I propose to enhance the Quality measure at Low Resolution (QLR) proposed in [12] by replacing the SSIM with a newly introduced CMSC Equation (4) [14] and additionally including sensor spectral response function gains to account for different spectral overlap of multispectral and panchromatic bands. Thus, QLR is defined in a reduced resolution space and compares only multispectral images which are spectrally overlapping with the panchromatic band…”

“…Thus, two new enhancements of [12] are introduced: A new FR measure CMSC and the spectral weights wk. Moreover, this measure is spectrally consistent or physically correct in that sense that it compares the commensurable information or the same parts (bands) of electromagnetic spectrum.…”

“…Moreover, it is incorrect physically or spectrally inconsistent because different spectrum parts or more generally content/information is compared, which may be incommensurable. Thus, I propose a Quality measure at High Resolution (QHR) which is defined at a high resolution scale [12]. This measure compares the intensity calculated from a weighted sum of multispectral bands (simulated panchromatic image) Imsf with the original pan image …”