Selection of numerical measures for pan-sharpening assessment

Abstract

Different tasks of multispectral image analysis and processing require specific properties of input pan-sharpened multispectral data such as spectral and spatial consistency. Generally, the quantitative measures for pan-sharpening assessment were taken from other topics of image processing (e.g. image similarity indexes). All these measures are widely employed for this task but the applicability basis of these measures is not checked and proven. In this paper a comparison of pan-sharpening assessment measures for remote sensing is carried out on specially generated pan-sharpened images. Performed statistical analysis on the assessment measures allows to select the measures which are most sensitive to the pan-sharpened imagery quality and these measures are recommended for use.

Quality assessment of pan-sharpening methods

“…Recent comparison [11,12] for various optical satellite data showed that the correlation CC or SSIM between original spectral bands and corresponding low pass filtered pansharpened bands is quite well suited for this purpose. It allows us to measure a spectral quality or preservation of a pan-sharpening method for individual bands or by averaging for all bands.…”

“…The same investigation [11,12] showed a preference of the structural similarity index measure SSIM between original panchromatic band (pan) and pan-sharpened bands (fms) for a spatial quality assessment. It exhibits high values (optimal value 1) for a high spatial quality and low values for low spatial quality.…”

“…In parallel to pan-sharpening methods development many attempts were undertaken to assess quantitatively their quality usually using measures originating from image processing such as Mean Squared Error (MSE), Correlation Coefficient (CC), Structural SIMilarity (SSIM) index [8] and finally recently proposed joint measures: product of spectral measure based on SSIM and spatial measure based on CC [9] and Quality with No Reference (QNR) measure [10]. Moreover, statistical evaluation of most popular pansharpening quality assessment measures was performed in [11,12] showing most relevant and practical quality measures using separate spectral and spatial image quality assessment over a broad collection of optical satellite sensor data. These results served as an inspiration for a new joint quality assessment measure (an enhanced version of a measure already presented in [4]) which is the main topic of this paper.…”

Fast, simple, and good pan-sharpening method

“…The same investigation 17,18 showed a preference of SSIM between original panchromatic band and pan-sharpened bands for a spatial quality assessment.…”

“…5 and 15. Recent comparison 17,18 showed that the correlation (CORR) between original spectral bands and corresponding low-pass filtered and subsampled pan-sharpened bands is one of the best. It allows us to measure a spectral quality or preservation of a pan-sharpening method for individual bands or by averaging for all bands…”