# Image Interpretability of nSight-1 Nanosatellite Imagery for Remote Sensing Applications
## 1 Introduction
Satellite image quality is a subject of considerable scientific interest because of its multifaceted nature and has been expressed using several technical parameters such as ground sampling distance (GSD), relative edge response (RER), edge extent (EE), line spread function (LSF), modulation transfer function (MTF) or point spread function (PSF), and signal to noise ratio (SNR) are used to quantitatively characterize the images in terms of sharpness, noise, nonlinearities, and artefacts [19; 20]. The National Image Interpretation Rating Scale (NIIRS) is another well-established image quality metric that is used for evaluating image interpretability and defines objects that are discernible in an image using a rating scale of 0 to 9 [21]. Markedly, the most influential parameters that affect image quality are GSD, MTF, and SNR [22; 23]. It is estimated that GSD contributes considerably (more than 70%) to visual image interpretability using the National Image Interpretation Rating Scale (NIIRS). The fifth version of the General Image Quality Equation (GIQE) shown below predicts the NIIRS is largely based on SNR, RER and GSD [22]. \[\text{NIIRS}=\text{A}_{0}+\text{A}_{1}\ast\text{Log}_{10}(\text{GSD})+\text{ A}_{2}\ast\Big{[}1-\exp\Big{(}\frac{\text{A}_{3}}{\text{SNR}}\Big{)}\Big{]}\ast \text{Log}_{10}(\text{RER})+\text{A}_{4}\ast\text{Log}10(\text{RER})^{4}+ \frac{\text{A}_{5}}{\text{SNR}} \tag{1}\]  
where:  
RER = Relative Edge Response  
GSD = Ground Sampled Distance  
SNR = Signal-to-Noise Ratio  
A\({}_{0}\) to A\({}_{5}\) = General Image Quality Coefficients 9.57, \(-\)3.32, 3.32, \(-\)1.9, \(-\)2, \(-\)1.8 respectively. Many attempts have been explored to predict NIIRS ratings from the images and their associated metadata [24]. The researchers point out that while the GIQE provides a good estimation of NIIRS, it does not account for atmospheric effects that affect image quality. [PERSON] [25] evaluated the image quality metric (IQM), just-noticeable-difference (JND) metric, peak signal-to-noise ratio (PSNR), root-mean-square error (RMSE), and maximum absolute difference (MAD) objective quality metrics to assess their predictive perceptual quality difference ratings. The assessment indicated that the IQM developed by [PERSON] et al. (1992) was highly correlated with NIIRS ratings and could be used as an indicator to predict the labor-intensive NIIRS ratings. Similarily, [PERSON] and [PERSON] [24] highlight that the IQM based method that predicts NIIRS ratings is reliable and performs well under a variety of conditions. The image quality measure (IQM) developed by [PERSON] [26] provides an objective and automated means of measuring that is highly correlated to NIIRS. The NIIRS ratings are obtained by applying the gradient and intercept values derived from the IQ-NIIRS regression equation [27]. A scale factor translates the power spectrum's inherent scale-independence to scale-dependence to convert the Image Quality to NIIRS ratings. A comparison of visual NIIRS and IQM yielded a correlation coefficient of 0.93. The spatial frequency power spectrum of digital images holds critical image quality information such as sharpness, contrast, and detailed rendition of the images [26]. Image quality assessments have the potential to provide a quality indicator of data captured by nanosatellites in the absence of metadata containing absolute or relative radiometric calibration information. In general terms, image quality approaches can also be classified to reference-based image quality assessment methods and blind or no-reference based algorithms [28]. While reference-based methods are dependent on pristine imagery as a reference source, blind, or automatic image quality assessment is feasible without the need for reference image through the application of objective blind algorithms that predict quality metrics in the distorted image by computing the deviations from the regularity of natural statistics [29; 30].