# Autonomous Learning Interactive Features for Hyperspectral Remotely Sensed Data
## 4 Experimental Results and Analysis
### Extraction of Vegetation and Water Body in the Washington DC Mall Dataset  
For the Washington DC Mall data set, because the number of samples is small, 4% of each type of feature is selected as the training sample, and the optimal regularization parameter obtained by 5-fold cross-validation is 25. At this time, the feature matrix is very sparse, with only one feature of water body and vegetation, as shown in Table 9:  
\begin{table}
\begin{tabular}{c c c} \hline
**Category** & **Recall of NDWI** & **Recall of BAND32** \\ \hline
0 & 0.998 & 0.997 \\
255 & 0.915 & 0.969 \\ Overall Accuracy & 0.989 & 0.994 \\ \hline \end{tabular}
\end{table}
Table 7: Quantitative comparison results of water extraction from the Pavia Center data set.  
\begin{table}
\begin{tabular}{c c c} \hline
**Category** & **Recall of NDVI** & **Recall of BAND48** \\ \hline
0 & 0.982 & 0.979 \\
255 & 0.783 & 0.833 \\ Overall Accuracy & 0.935 & 0.945 \\ \hline \end{tabular}
\end{table}
Table 8: Quantitative comparison results of vegetation extraction from the Pavia Center data set.  
Figure 6: Extraction results of vegetation and partial enlarged image: (**a**) Ground truth information; (**b**) NDVI extraction result; (**c**) BAND48 extraction result.  
\begin{table}
\begin{tabular}{c c c} \hline
**Category** & **Interactive Features** & **Coefficient** \\ \hline Water & BAND9 & 0.022 \\ Meadow & BAND41 & \(-\)0.09 \\ \hline \end{tabular}
\end{table}
Table 9: Interaction characteristic coefficients of water body and vegetation.  
BAND9 and BAND41 can be expressed as:  
\[\text{BAND9}=\frac{\text{band6}-\text{band95}}{\text{band6}+\text{band95}}=\frac{ purple-NIR}{purple+NIR} \tag{11}\]  
\[\text{BAND41}=\frac{\text{band26}-\text{band85}}{\text{band26}+\text{band85}}= \frac{G-NIR}{G+NIR} \tag{12}\]  
The wavelength of band6 is 418 nm, which corresponds to the purple light band, so it is named \"purple\"; the wavelength of band95 is 1248 nm, which corresponds to the NIR band. The wavelength of band26 is 498 nm corresponding to the G band, and the wavelength of band85 is 1099 nm corresponding to the NIR band. The above two expressions seem to be inconsistent with those of NDVI and NDWI. To verify the experimental results, BAND9 and BAND41 were used to extract water body and vegetation respectively. Then BAND9 is compared with NDWI in terms of water extraction; BAND41 is compared with NDVI in terms of vegetation extraction, as shown in Figures 7 and 8:Figure 8: _Cont._  
Figure 7: Extraction results of water body and partial enlarged image: (**a**) Ground truth information; (**b**) NDWI extraction result; (**c**) BAND9 extraction result.  
From visual judgment, both the effect of BAND9 in extracting water body and the effect of BAND41 in extracting vegetation are similar to those of NDWI and NDVI. To display the extraction results more clearly, the extraction results of water and vegetation are also quantified. The quantitative results of water body are shown in Table 10; the quantitative results of vegetation are shown in Table 11.  
It can be seen from Table 10 that for the extraction of water body, the extraction overall accuracy of BAND9 and NDWI are almost the same as 0.893, but for the water body category (pixel value 255), BAND9's 0.251 is better than NDWI's 0.209; from Table 11 we can see that for vegetation, the number of pixels correctly extracted by BAND41 is less than NDVI, but the overall accuracy of BAND41 is better than NDVI.