# Image Interpretability of nSight-1 Nanosatellite Imagery for Remote Sensing Applications
## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**nSight-1 Image** & **Location** & **Discernible Features** & **Application** \\ \hline Klipplaat, South Africa & Geological structures, lineaments, intrusions, bedding trends, lithology, and boundaries; soil erosion features; river system; dryland or degraded landscape & Geological mapping, land degradation, and soil erosion assessments \\ \hline Bolivia Salar de & Soil erosion; & Land degradation \\ Uyuni & geological folding; & and soil erosion \\ dryland or degraded & assessment; \\ landscape; & geological \\ drainage pattern & mapping \\ morphology & & \\ \hline Saudi, Mecca & Road network; built-up areas & Urban mapping; mapping of major roads \\ \hline \hline \end{tabular}
\end{table} 
Table 5: \(Cont.\)The good visual quality of the nSight-1 images in the spatial dimension suggest that the image is potentially valuable in producing accurate land use and land cover maps using on-screen digitizing and other advanced classification approaches such as object-based classification and morphological profiles that exploit spatial information and image characteristics such as shape, size, colour, compactness, and texture. The variability in the NIIRS could be a result of several factors. While NIIRS values are largely influenced by ground sampling distance and relative edge response, other variables such as the signal to noise ratio, noise gain due to edge sharpening, and geometric mean height of overshoot due to edge sharpening plays a role. The impact of variable factors, such as atmospheric effects on the images due to haze has the potential to affect the results. The results, however, seem to show overall consistency in terms of the NIIRS variability trends between nSight-1 and the reference Landsat 8 data. This may suggest that environmental conditions such as structural landscape features, terrain, build-up features, atmospheric conditions, and illumination conditions affect the assessment of NIIRS values. This was particularly evident in the Okavango Delta, where high values were obtained and the Saudi Arabia Desert where low NIIRS values were derived from both n-Sight-1 and Landsat 8 data. This result was line with our visual observation, that is, in cases where large landscape features are conspicuously defined with a good contract the NIIRS values were high. This was also true in desert environments \begin{table}
\begin{tabular}{c c c c} \hline \hline
**nSight-1 Image** & **Location** & **Discerinble Features** & **Application** \\ \hline  & Loskop, South Africa & Drainage pattern morphology; vegetation patches; river system; cropped and fallor agricultural fields & Drainage analysis; water resources management; land degradation assessment; land cover mapping \\ \hline  & Loskop, South Africa & Concentric cropped and fallor agricultural fields; dam; river system; open grasslands; roads & Agriculture; water resources management; land cover classification; mapping of major roads \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._with little contrast, we observed low NIIRS values. This suggests contracting grey-level values driven by tonal variations resulting from background effects might have a bearing on NIIRS. Considering the small size of the nanosatellite, the analyses of the nSight-1 images undertaken in this study clearly indicates that the RGB images captured by the Gecko Imager are of high quality and are useful for a variety of cartographic and geo-visualization applications. Success could be attributed the innovative matrix design of the Gecko Imager that uses a Bayer-pattern filter to capture colour images at medium spatial resolution of 30 m, high native radiometric resolution 12 bits that are downsampled to 8-bits and nanosatellite's robust real-time onboard computer processing capabilities. The spatial resolution of the images captured by the Gecko matrix imager could be increased through super-resolution (SR) image reconstruction as demonstrated by [PERSON] [28] using the ZY-3 TLC images.