# HYPSO-1 CubeSat: First Images and In-Orbit Characterization
## 3 In-Orbit Payload Performance
### Spectral Signal-to-Noise Ratio  
The SNR of HYPSO-1 is estimated using two different approaches and compared to four other remote sensing instruments: the multispectral 3U CubeSat-SeaHawk, Airborne Visible-Infrared Imaging Spectrometer-Next, Generation (AVIRIS-NG), PRecurscore Iperp-Spettrale della Missione Applicativa (PRISMA), and Hyperspectral Imager for the Coastal Ocean (HICO). All four sensors used for comparison produce images that have a spectral resolutions similar to HYPSO-1. The comparison only includes the spectral range that matches the HYPSO-1 payload. When interpreting the results, recall that the HYPSO-1 data are binned spectrally by summation from 1080 pixels to 120 spectral bins. Because the data sets used do not have a ground truth, the estimated SNR characterizes the precision of the radiometric measurements and does not provide any insight concerning the accuracy of the measured signals. The results presented in this section do not attempt to compensate for the viewing and solar zenith angles of each platform during the image acquisition of the different scenes. Table 8 gives relevant information on the sensors. The scenes used in this analysis are presented in Table 9. Information on the mass of each sensor platform is added to indicate the launch cost of the respective platform. Both approaches for noise estimation are intended to be used over homogeneous areas [28; 50; 51]. These homogeneous areas are used as the difference in the signals can be mostly attributed to sensor-level noise. The manually selected homogeneous regions of the different data sets are marked with a white dashed box in Figure 14, while the distribution of those signals is given in Figure 15.  
Figure 13: The multiplicative correction applied to the radiometric calibration in order to destripe acquired hyperspectral images (**left**). The first PCA component of the ocean pixels from the test scene without destriping (**right, top**), the first component computed after the image has been destriped (**middle**), and the seventh component in the destriped image (**bottom**). The image shows Frohavet, outside of Trondheim, Norway. Figure 14: RGB renders of the data sets used for SNR estimation. The square dashed white boxes mark the regions that estimate SNR. See Table 9 for details. The RGB renders are cropped and scaled to spatially have a similar aspect ratio. The bands used for the RGB composite representation are the ones closest to 602.5 nm, 565 nm, and 498.4 nm, respectively, for the given sensor. The distribution of the signals are given in Figure 15.  
Figure 15: Signal distribution of selected areas in Figure 14 used to assess SNR. Solid lines show mean signal, and dashed lines show two standard deviations from mean signal. All signals are normalized to be between 1 and 0 prior to computing the mean and standard deviation. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sensor** & **HYPSO-1** & **HICO** & **AVIRIS-NG** & **PRISMA** & **SeaHawk** \\ \hline
**FWHM** & 8.2 nm & 5.7 nm & 5 nm & 10 nm & 20 nm \\
**Bands** & 120 & 74 & 84 & 74 & 8 \\
**Mass (kg)** & 7.1 & 41 & 465 & 830 & 6 \\
**Reference** & [5] & [8] & [18,52] & [7,17,38] & [19] \\
**Processing** & Binning & Smoothing & Orthorectified & Orthorectified & Orthorectified \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Relevant sensor characteristics. The listed number of bands includes only those within the spectrum of the HYPSO-1 instrument. The mass refers to the weight of the platform, with an exception for the HICO as it was onboard the International Space Station (ISS). Firstly, as described in [51], the noise is estimated using an intermediate processing step of subspace identification. The underlying assumption of this method is that the radiance or reflectance at a given spectral band can be modeled by linear regression of the remaining bands. Here, \(\mathbf{X}_{o}\) is used to denote the different spectra as matrices with each observation along the rows.