Abstract
Remote sensing images are subject to different types of degradations. The visual quality of such images is important because their visual inspection and analysis are still widely used in practice. To characterize the visual quality of remote sensing images, the use of specialized visual quality metrics is desired. Although the attempts to create such metrics are limited, there is a great number of visual quality metrics designed for other applications. Our idea is that some of these metrics can be employed in remote sensing under the condition that those metrics have been designed for the same distortion types. Thus, image databases that contain images with types of distortions that are of interest should be looked for. It has been checked what known visual quality metrics perform well for images with such degradations and an opportunity to design neural network-based combined metrics with improved performance has been studied. It is shown that for such combined metrics, their Spearman correlation coefficient with mean opinion score exceeds 0.97 for subsets of images in the Tampere Image Database (TID2013). Since different types of elementary metric pre-processing and neural network design have been considered, it has been demonstrated that it is enough to have two hidden layers and about twenty inputs. Examples of using known and designed visual quality metrics in remote sensing are presented.
Highlights
There are a great number of applications of remote sensing (RS) [1,2]
The analysis shows that all metrics become worse (PSNR, PSNRHA and the designed metric decrease, and Mean Deviation Similarity Index (MDSI) increases) if noise variance increases, i.e., the monotonicity property is preserved
Its use allows the determination of existing visual quality metrics that perform well for the types of degradations that are of interest
Summary
There are a great number of applications of remote sensing (RS) [1,2]. There are many reasons behind this [3,4]. We concentrate on metrics characterizing the quality of original images or images after pre-processing, such as denoising or lossy compression, focusing on full-reference metrics and, in particular, visual quality metrics Conventional metrics, such as mean square error (MSE) or peak signal-to-noise ratio (PSNR), are still widely used in the analysis of RS images or evaluation of the efficiency of their processing [29,32,33,34].
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.