# Image Interpretability of nSight-1 Nanosatellite Imagery for Remote Sensing Applications
## 4 Results and Discussion  
The results for the blind image spatial quality assessment and the NIIRS evaluation are shown in Tables 1 and 2 for nSight-1 and Landsat 8 respectively. The Shapiro-Wilk test results confirmed a normal distribution for the image quality score and NIIRS variables for both nSight-1 and Landsat 8. The normality test for the NIIRS values using an alpha value of 0.05 was positive as shown by a \(p\)-value of 0.8651 and 0.7949 for nSight-1 and Landsat 8 respectively. The Shapiro-Wilk test \(p\)-values for the visual scores were 0.3314 and 0.3469 for the nSight-1 and Landsat 8 respectively. The two-tailed \(t\)-test results indicate that Landsat 8 OLI images yielded significantly higher visual quality scores and NIIRS results compared to Landsat 8 OLI, indicative of Landsat 8's superior image quality and better image interpretation compared to nSight-1. The mean for Landsat 8 is 19.299 and nSight-1 it is 25.873 for the image quality score. Landsat 8 had NIIRS mean of 2.345 while nSight-1 had a mean of 1.622. The statistical significance of between the image quality scores is reflected by a \(p\)-value of 0.0373 and NIIRS for NIIRS assuming a 95% confidence interval. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Image Location** & **Image Quality Score** & **Categorical 5 Level Scale** & **NIIRS** \\ \hline Bolivia, Salar de Uyumi & 36.3078 & Good & \\ \hline Botswana, Okavango & 36.831 & Good & 1.77501 \\ \hline Canada & 14.4916 & Excellent & 2.08289 \\ \hline Egypt & 37.5298 & Good & \\ \hline Greece & 20.5761 & Good & 1.8977 \\ \hline Saudi Arabia, Mecca & 10.4605 & Excellent & 0.616793 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: nSight-1 image quality and interpretability assessment  
a GDS of 30-m compared to 32-m on nSight-1. GSD is considered to be one of the most influential factors affecting NIIRS. The image degradation that occurs due to the lossy compression implemented on nSight-1 from 12-bit to 8-bit can be considered to lower its image visual quality and interpretability. Whereas it is evident that Landsat 8 has better visual quality and NIIRS scores, the results reveal that when considered independently nSight-1 images are easily interpretable at a NIIRS rating of 1.6 and of good spatial image quality as attested by its mean score of 26 obtained in the refereenceless evaluation. Moreover, at the categorical level, the nSight-1 images were largely rated as good to excellent. The visual-spatial quality of nSight-1 imagery could be attributed to the spatial, spectral, and radiometric characteristics of the data. Clarity and identification of objects are substantially influenced by salient image quality parameters such as GSD, MTF, SNR, RER, LSF, and EE. Variations in the NIIRS values obtained in this study could be attributed to factors such as atmospheric haze, contract, angle of obliquity and noise that influence image interpretability. Our visual interpretation results are consistent with NIIRS ratings between 1 and 2.5 computed in this study. At NIIRS 1, we were able to distinguish between key land covers and land use classes such as agriculture, forests, urban and rural areas, geological structures, and drainage morphology; and at NIIRS 2, we identified road networks, large ships, large centre-pivot irrigated fields. It is evident from this study that a medium spatial resolution of 30 m is suitable for discriminating land cover features such as agricultural fields, water bodies, forest patches, settlements, coastline, and roads. Equally important, the spectral resolution of the imagery in the visible range was fundamental in differentiating the subtle colour variability that reflects the composition within various land cover types. In this case, the location of the imager's spectral bands in the principal (blue, green, and red) wavelengths regions of the visible electromagnetic spectrum proved sufficient for visual analysis. The blue, green and red spectral bands on the nSight-1 imager are valuable for a range of remote sensing applications.