# Autonomous Learning Interactive Features for Hyperspectral Remotely Sensed Data
## 4 Experimental Results and Analysis
### Extraction of Vegetation and Water Body in the Pavia Center Data Set  
For the Pavia Center data set with the interaction between bands, 0.5% of each type of feature is selected as the training sample. If the regularization coefficient is too large, it will easily cause under-fitting, and if it is too small, it will easily cause over-fitting. Therefore, the most suitable regularization coefficient is selected automatically through 5-fold cross-validation and controls the value of the regularization coefficient within the  
Figure 4: False-color composite map of Washington DC Mall and ground truth information: **(a)** False-color composite image; **(b)** Ground truth image. range of 0.01 to 100, and the number is set to 30. When the cross-validation error is the smallest, the value of the optimal regularization parameter is 16.7, Since the water body information in the data set is relatively rich, the feature selection for the water body type is shown in Table 4:  
88, 94, 101 represent the original band88, 94, and 101, and the corresponding wavelengths are 865 nm, 895 nm, and 930 nm, which belong to the NIR band; BAND13, BAND32, and BAND55 indicate the bands that are added to the interaction by multiplication or division operation. The naming rules for the interaction bands are: for example, the original bands remaining after dimensionality reduction are a band \(a\), band \(b\), band \(c\), band \(d\), and band \(e\), then BAND1 is expressed as the interaction between band \(a\) and band \(b\), BAND2 It is expressed as the interaction between band \(a\) and band \(c\) until BAND4 is expressed as the interaction between band \(a\) and band \(e\), the interaction between band \(a\) and other bands except itself indicates the end, and then BAND5 indicates the interaction between band \(b\) and band \(c\) until the interaction of band \(b\) and other bands except itself indicates the end, and so on until BAND10 indicates the interaction of band \(d\) and band \(e\), so far all the interaction indicates completion. BAND13 means (band11-band44)/ (band11 + band44). The wavelength of band11 is 480 nm and belongs to the B (blue) band. The wavelength of band44 is 645 nm and belongs to the R band. So BAND13 describes the interaction between B and R bands. In the same way, BAND32 means (band34-band88)/ (band34 + band88) and describes the relationship between G (green) and NIR bands; BAND55 means (band94-band101)/(band94 + band101) and describes the interaction between the NIR bands. The specific interaction relationship is shown in Table 5:  
From the coefficients of multiclass logistic regression, BAND32 has the most prominent regression coefficient among all the characteristics, indicating that BAND32 is the most sensitive to water body information and has the best effect on water body recognition. Therefore, BAND32 is used as an interactive water index, which is expressed as follows:  
\[\text{BAND32}=\frac{\text{band34}-\text{band88}}{\text{band34}+\text{band88}}= \frac{G-NIR}{G+NIR} \tag{9}\]  
The same method for the feature selection of the feature type of tree is shown in Table 6:  
BAND47 represents the interaction between band66 and band88, the wavelength of band66 is 755 nm, which means the R band, and the wavelength of band88 is 865 nm, which represents the NIR band. In the same way, BAND48 and BAND49 are also expressed as the interactive relationship between the R band and the NIR band. Since the regression coefficients of BAND47 and BAND48 are larger than BAND49, it means that for vegetation \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Features** & **BAND47** & **BAND48** & **BAND49** \\ \hline Coefficient & 0.769 & 0.800 & 0.094 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Interactive feature coefficients of vegetation. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Features** & **88** & **94** & **101** & **BAND13** & **BAND32** & **BAND55** \\ \hline Coefficient & \(-2.189\) & \(-2.941\) & \(-1.082\) & \(-0.751\) & 5.706 & 1.574 \\ \hline \hline \end{tabular}
\end{table}