# Autonomous Learning Interactive Features for Hyperspectral Remotely Sensed Data
## 5 Conclusions  
This paper proposes a data-driven method to learn interactive features for hyperspectral remotely sensed data based on a sparse multiclass logistic regression model. The key point is explicitly to express the interaction relationship between original features as new features by multiplication or division operation, and the strong constraint effect of the L1 norm makes feature sparseness. The size of the coefficient value of the corresponding feature after sparse is used as the basis for judging the importance of the feature, then the most important interactive feature is used as the index for extracting ground features. Significantly, small samples are taken on three hyperspectral remotely sensed data sets. These experiments were carried out separately, and the following conclusions were drawn:  
(1) The interaction automatically learned through the data-driven method as a ground feature index can effectively extract water and vegetation. The interaction is expressed by multiplication or division operation between original features. It is shown in the Pavia Center data set that the interactive features BAND32 can extract water body information availably, BAND48 can effectively extract vegetation information; in the Washington DC Mall data set, BAND9 can effectively extract water information, which is almost indistinguishable from NDWI. Although BAND41 does not perform very well with NDVI, it can still extract most of the vegetation information; in the Pavia University data set, the effect of vegetation information extraction with BAND19 is better than NDVI. These  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **BAND22** \\ \hline
0 & 0.995 \\
255 & 0.848 \\ Overall Accuracy & 0.994 \\ \hline \hline \end{tabular}
\end{table}
Table 17: Quantitative comparison results of metal sheets extraction from the Pavia university data set.  
Figure 10: Extraction results of metal and partial enlarged image: (**a**) Ground truth information; (**b**) BAND22 extraction result.  
interactive relationships learned by the data-driven method can be used as a new type of feature index, which is of great significance to the development of feature recognition.  
(2) A new type of metal index BAND22 is proposed in the Pavia University data set, and it is verified with ground truth information of this category. The experimental result proves that the index can effectively extract the ground feature of metal.  
The ground feature types explored in this study are only water body and vegetation. In the future, more ground feature types will need to be further studied in order to learn more ground feature indexes. If the verification is effective, it will have pivotal significance on ground features recognition and even social production activities.  
Conceptualization, L.D. and G.Z.; methodology, L.D. and G.Z.; software, L.D., J.G. and R.Z.; validation, L.D., G.Z., J.G. and R.Z.; formal analysis, L.D. and G.Z.; investigation, L.D. and G.Z.; writing--original draft preparation, L.D. and G.Z.; writing--review and editing, L.D., G.Z., J.G. and R.Z.; supervision, G.Z., J.G., and R.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data sets used in the article are open source.  
The authors declare no conflict of interest.