# Multiband and Lossless Compression of Hyperspectral Images
## 4 Experimental Results
### CCSDS Dataset  
In this section we focus on the experimental results we have achieved by considering the CCSDS Dataset, which is composed by five calibrated and seven uncalibrated hyperspectral images. This dataset is provided by Consultative Committee for Space Data Systems (CCSDS) Multispectral and Hyperspectral Data Compression [27]. In Table 11, we shortly describe the dataset by reporting the number of scenes (second column) and the number of samples per line (third column) for the calibrated and the uncalibrated images (first column). The samples of the calibrated and the uncalibrated images are stored by using 16 bits (16-bit signed integer for the calibrated and 16-bit unsigned for the uncalibrated), except for the _Hawaii_ and _Maine_ images in which the samples are stored by using 12 bits (unsigned) [27]. Each image is composed by 512 lines. In Table 12, we report our results in terms of bits-per sample (BPS). The results refer to the calibrated hyperspectral images (first column), by using several configurations for our approach (columns from the second to the fourth). Analogously to Table 12, in Table 13 we report our experimental results for the uncalibrated images. In each table, we report the results by using the AC and the PAQ8 schemes. The best results are achieved when the following configuration is used: \(B=~{}2\) and \(N=16\). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Images** & **Num. of Scenes (_Denotation of the Scenes_)** & **Samples** \\ \hline \multicolumn{3}{c}{_Calibrated_} \\ \hline Yellowstone & 5 scenes (3, 10, 11, 18) & 677 \\ \hline \multicolumn{3}{c}{_Uncalibrated_} \\ \hline Yellowstone & 5 scenes (3, 10, 11, 18) & 680 \\ Hawaii & 1 scene (I) & 614 \\ Maine & 1 scene (10) & 680 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Description of the CCSDS dataset. \begin{table}
\begin{tabular}{c|c c|c c|c c} \hline \hline
**Images/Configurations** & \multicolumn{2}{c|}{\(N=8\), \(B=1\)} & \multicolumn{2}{c|}{\(N=8\), \(B=2\)} & \multicolumn{2}{c}{\(N=16\), \(B=2\)} \\ \hline  & **AC** & **PAQ8** & **AC** & **PAQ8** & **AC** & **PAQ8** \\ \hline Yellowstone 0 & 4.1881 & 4.0898 & 4.0435 & 3.9917 & 3.9783 & 3.9198 \\ Yellowstone 3 & 4.0831 & 3.9658 & 3.9449 & 3.8701 & 3.8795 & 3.7972 \\ Yellowstone 10 & 3.5470 & 3.3974 & 3.4712 & 3.3488 & 3.3925 & 3.2592 \\ Yellowstone 11 & 3.7878 & 3.7128 & 3.7035 & 3.6554 & 3.6311 & 3.5751 \\ Yellowstone 18 & 4.1495 & 4.0350 & 3.9773 & 3.9154 & 3.9082 & 3.8403 \\ \hline Average & 3.9511 & 3.8401 & 3.8280 & 3.7563 & 3.7579 & 3.6783 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Achieved results for the calibrated images of the CCSDS dataset. Comparison with Other Approaches  
We have compared our results on the CCSDS dataset with other state-of-art approaches. Table 14 reports the results achieved by considering several values for the \(B\) and \(N\) parameters on the calibrated hyperspectral images of the CCSDS dataset, by using the AC scheme as well as the PAQ8 scheme for the coding of the prediction errors. Tables 15 and 16 report the comparison between the proposed approach and other approaches for the 16-bit uncalibrated and 12-bit uncalibrated hyperspectral images of the CCSDS dataset. From Tables 12 and 13 it comes clear that the best results are achieved when the value of \(N\) is equal to 16 and \(B\) is equal to 2. By looking at Table 14, it should be observed that our approach, when the following configuration is used: \(N=16\) and \(B=2\), achieves results that are comparable but slightly worse with respect to FL and FL# [27]. Our approach outperforms all the other techniques when the PAQ8 scheme is used for the coding of prediction errors with \(N=16\) and \(B=2\). However, in such configuration the computational complexity of our approach is not suitable for on board implementations.