Standardizing Quality Assessment of Fused Remotely Sensed Images

Abstract

ABSTRACT:The multitude of available operational remote sensing satellites led to the development of many image fusion techniques to provide high spatial, spectral and temporal resolution images. The comparison of different techniques is necessary to obtain an optimized image for the different applications of remote sensing. There are two approaches in assessing image quality: 1. Quantitatively by visual interpretation and 2. Quantitatively using image quality indices. However an objective comparison is difficult due to the fact that a visual assessment is always subject and a quantitative assessment is done by different criteria. Depending on the criteria and indices the result varies. Therefore it is necessary to standardize both processes (qualitative and quantitative assessment) in order to allow an objective image fusion quality evaluation. Various studies have been conducted at the University of Osnabrueck (UOS) to establish a standardized process to objectively compare fused image quality. First established image fusion quality assessment protocols, i.e. Quality with No Reference (QNR) and Khan's protocol, were compared on varies fusion experiments. Second the process of visual quality assessment was structured and standardized with the aim to provide an evaluation protocol. This manuscript reports on the results of the comparison and provides recommendations for future research.

Quality Assessment by Region and Land Cover of Sharpening Approaches Applied to GF-2 Imagery

“…This is consistent with the results of previous studies [7,12], as well as previous results for the GS and PCA pansharpening methods [23,30,32,36,46,50,51]. It is also consistent with previous results for the mIHS and PCA pansharpening methods [48,52] and the mIHS, GS, and PCA pansharpening methods [53]. However, it is inconsistent with the findings of Sarp, who reported that the PCA method produced better results than the GS pansharpening method for the difference in the correlation coefficient and root mean square error (RMSE) values between the images before and after fusion in both IKONOS and QuickBird in Istanbul [30].…”

“…These results are consistent with those of Witharana et al, who reported that for GeoEye-1 urban images, the CN method exhibited the highest scores for spatial metrics, followed by the mIHS, GS, and PCA methods, although different spatial metrics, such as the canny edge detection filter, the high-pass correlation coefficient, and the RMSE of a Sobel-filtered edge image, were used [37]. The results are also consistent with previous studies in which the mIHS method achieved better scores for spatial information than the PC method [32,52].…”

Critical Reflection on Quantitative Assessment of Image Fusion Quality

“…Attention should be paid to these factors when statistical indices are used for the quantitative measurement of fusion quality. As some research has pointed out, one direction for future research is a standardized visual quality assessment to objectively compare fusion quality (Pohl et al, 2017). It might be useful to extend such a quality assessment and develop more robust quantitative measurements.…”

Perceptual Quality Assessment of Pan-Sharpened Images

“…The reference images and fused images were displayed side by side (a double-stimulus (DS) study) to enable the subjects to rank the images produced by the various methods. In a different approach presented in [37], the authors developed a visual quality assessment protocol (VQAP) in which the human evaluator is guided through the process of fused image assessment from global to local features. Criteria such as sharpness, color preservation, and object recognition support the judgment on the quality of the images.…”