Perceptual Quality Assessment of Pan-Sharpened Images

Abstract

Pan-sharpening (PS) is a method of fusing the spatial details of a high-resolution panchromatic (PAN) image with the spectral information of a low-resolution multi-spectral (MS) image. Visual inspection is a crucial step in the evaluation of fused products whose subjectivity renders the assessment of pansharpened data a challenging problem. Most previous research on the development of PS algorithms has only superficially addressed the issue of qualitative evaluation, generally by depicting visual representations of the fused images. Hence, it is highly desirable to be able to predict pan-sharpened image quality automatically and accurately, as it would be perceived and reported by human viewers. Such a method is indispensable for the correct evaluation of PS techniques that produce images for visual applications such as Google Earth and Microsoft Bing. Here, we propose a new image quality assessment (IQA) measure that supports the visual qualitative analysis of pansharpened outcomes by using the statistics of natural images, commonly referred to as natural scene statistics (NSS), to extract statistical regularities from PS images. Importantly, NSS are measurably modified by the presence of distortions. We analyze six PS methods in the presence of two common distortions, blur and white noise, on PAN images. Furthermore, we conducted a human study on the subjective quality of pristine and degraded PS images and created a completely blind (opinion-unaware) fused image quality analyzer. In addition, we propose an opinion-aware fused image quality analyzer, whose predictions with respect to human perceptual evaluations of pansharpened images are highly correlated.

A Benchmarking Protocol for Pansharpening: Dataset, Preprocessing, and Quality Assessment

“…Other quality metrics have been proposed in [14], which adopts the spectral distortion borrowed from [11] and the spatial distortion based on the natural image quality evaluator model, and in [15], which presents a perceptual quality index.…”

A Unified Framework for Anomaly Detection of Satellite Images Based on Well-Designed Features and an Artificial Neural Network

“…Xia et al [18] reviewed and concluded the quality assessment for remote sensing images. Agudelo-Medina [19] proposed a new IQA measure that supports the visual qualitative analysis of pan-sharpened outcomes by using the statistics of natural images (commonly referred to as natural scene statistics), to extract statistical regularities from pan-sharpened images. A no-reference hyperspectral IQA method based on quality-sensitive features extraction was presented by Jingxiang [20].…”

An Optimal Higher Order Likelihood Distribution Based Approach for Strong Edge and High Contrast Restoration

“…The restoration of the edge component deteriorates when external noise is introduced. In this paper for numerical analysis we have considered performance metrics like Entropy (E), Correlation coefficient (ℂ), Local texture energy (ℓ) , peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM) [34][35][36][37][38]. Entropy ( ) is the information content of image and it is represented as…”

“…Local texture energy (ℓ) is an image quality assessment parameter that measures distortions affecting the image textures [34,35]. We have used this metric to measure any impairment caused by contrast or edges.…”