## 1 Introduction  
Modern remote sensing (RS) systems produce an enormous number of images that are later used for many valuable applications such as ecological monitoring, agriculture, and urban planning [1; 2; 3]. It is often supposed that all RS images are of high quality. However, this is not so due to many reasons. Noise and other distortions can be present in acquired data due to the RS sensor principle of operation such as speckle in radar images [4], wave absorption in specific bands as in junk channels of hyperspectral data [5], bad conditions of imaging [6], and imaging or communication system failures [7]. Then, an important task is to estimate an image quality for an original (acquired) image or a processed (e.g., filtered) image [6; 8; 9; 10; 11]. There are practical situations when full-reference metrics [12; 13] can be employed for this purpose [8]. This happens when there is an image that can be considered as \"pristine\" (reference) and, after processing (e.g., lossy compression), one has the corresponding distorted image that should be compared to the reference one using a certain quality metric where both traditional (such as Mean Square Error--MSE or Peak Signal-to-Noise Ratio--PSNR) or visual quality (e.g., Structural Similarity--SSIM [14; 15]) metrics can be applied. Nevertheless, one can often meet situations where full-reference metrics cannot be used since reference images are unavailable. Then, there are two options: either to try predicting the values of trustworthy full-reference metrics or apply no-reference or reduced-reference ones. Examples of the first option are given in the papers [16; 17], where trained neural networks (NNs) are applied to characterize the quality of images, including the quality of synthetic aperture radar (SAR) images, by predicting the values of different image quality metrics. Such an approach is based on the known fact that full-reference metrics characterize the quality of distorted images better than no-reference ones where accuracy is usually described in terms of conventional or rank correlation of a metric and Mean Opinion Score (MOS) for databases of distorted images [12]. However, the values of full-reference metrics are anyway predicted with errors and the already proposed solutions are not universal. Because of this, an alternative option is often used, i.e., to apply no-reference (NR) metrics [18; 19; 20; 21; 22; 23]. NR metrics designed earlier (e.g., [19; 20]) have mainly addressed particular types of distortions such as blur, noise, and blockiness caused by lossy JPEG compression with application to optical images. Being applied to a wide set of possible distortions such as in the database TID2013, such NR metrics fail to provide good results [24]. Even for the best metrics, such as IL-NIQE [25] and NIQE [26], the values of Spearman Rank Order Correlation Coefficient (SROCC) between the metric and Mean Opinion Score values do not exceed 0.5 for the database TID2013 [27], although SROCC values are larger for simpler databases as LIVE [28]. On the contrary, with the development of modern technologies and machine learning-based approaches, image quality assessment (IQA) methods have become more complicated and powerful. Support vector machines, neural networks, and other approaches [18; 22; 23; 24] have been employed to improve the performance of NR metrics used for IQA for conventional (grayscale and color) images as well as RS images [17; 22; 23]. The authors of [18] have designed an efficient classifier of distortion types based on support vector machine (SVM) and a set of input features. They have tested it for several available databases. However, they have considered a limited set of three distortion types. Analysis of the 21 NR metrics for Quickbird RS images has been carried out in [22], where the edge intensity metric has been recommended for practical use. However, the study has been performed for only three types of distortions (average filtering, Gaussian white noise, and linear motion degradation).