## 4 Conclusions  
There always exists information corresponding to each channel (wavelength) during the acquisition process in hyperspectral imaging (HI). Since diffuse scattering and reflection are specific to material dependence, this dimension has enormous applications in biomedical, geography, defense, and chemistry. In this paper, a novel methodology is introduced that is called hybrid learning system (HLS), which categorizes hyperspectral images into segmented regions with discriminative features using reduced training size. HLS is a semi-supervised learning approach using active learning, a supervised learning technique, and unsupervised learning-based MLR for classification and segmentation of HI. The proposed method outperformed with a fewer number of training instances. For merged spatial and spectral information, EMAPS are computed first for each dataset to give improved classification results. Generalized composite kernels have been used in  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Sr. No.**} & \multirow{2}{*}{**References**} & \multicolumn{3}{c}{**Classification Accuracy**} & \multicolumn{3}{c}{**Segmentation Accuracy**} \\ \cline{3-8}  & & **OA** & **AA** & **Kappa** & **OA** & **AA** & **Kappa** \\ \hline
1 & [PERSON] et al. [28] & 2.15 & 3.08 & 0.80 & 3.15 & 5.89 & 1.21 \\ \hline
2 & [PERSON] et al. [30] & 12.84 & 67.87 & 9.54 & 14.30 & 77.50 & 10.42 \\ \hline
3 & [PERSON] et al. [31] & 42.91 & 62.01 & 18.24 & 48.83 & 66.13 & 22.99 \\ \hline
4 & [PERSON] et al. [32] & 43.02 & 31.73 & 8.18 & 40.14 & 38.81 & 10.78 \\ \hline
5 & Proposed HLS & 99.14 & 98.56 & 99.01 & 99.42 & 99.29 & 99.28 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of classification and segmentation accuracies of the Pavia University dataset (class-wise) on 10 repetitions using 5 training samples of each class.  
the MLR algorithm for classification to produce the posterior class probabilities against each instance for better segmentation of landscapes. The comparison of work has been carried out using benchmark datasets, i.e., \"Indian Pines\" and \"Pavia University\" with known methodologies. The HLS based HI has been found to yield an overall accuracy of [99.93% and 99.98%] Indian Pines and [99.14% and 99.42%] Pavia University for classification and segmentation, respectively.  
Conceptualization, S.A.Q., S.T.H.S., A.u.R. and A.A. ([PERSON]); methodology, S.A.Q., S.T.H.S., A.u.R., M.R. and A.A. ([PERSON]); software, S.A.Q. and S.T.H.S.; validation, S.A.Q., S.T.H.S., S.A.H.S., A.u.R., A.A. ([PERSON]), D.A.B., A.A. ([PERSON]), A.A.M., M.U.K., M.R.I.F. and M.R.; formal analysis, S.A.Q., S.T.H.S., A.u.R., A.A. ([PERSON]), D.A.B., A.A. ([PERSON]), A.A.M., M.U.K., M.R.I.F. and M.R.; investigation, S.A.Q., S.T.H.S., S.A.H.S., A.u.R., A.A. ([PERSON]), D.A.B., A.A. ([PERSON]), A.A.M., M.U.K., M.R.I.F. and M.R.; resources, S.A.Q. and S.T.H.S.; data curation, S.A.Q., S.T.H.S., A.H.S., A.u.R., A.A. ([PERSON]), D.A.B., A.A. ([PERSON]), A.A.M., M.U.K., M.R.I.F. and M.R.; writing--original draft preparation, S.A.Q. and S.T.H.S.; writing--review and editing, S.A.Q., S.T.H.S., S.A.H.S., A.u.R., A.A. ([PERSON]), D.A.B., A.A. ([PERSON]), A.A.M., M.U.K., M.R.I.F. and M.R.; visualization, S.A.Q., S.T.H.S., A.H.S., A.u.R., A.A. ([PERSON]), D.A.B., A.A. ([PERSON]), A.A.M., M.U.K., M.R.I.F. and M.R.; supervision, S.A.Q.; project administration, S.A.Q.; funding acquisition, D.A.B. and A.A. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
The authors acknowledged the APC contribution by the Deanship of Preparatory Year and Supporting Studies, Imam Abdulrahman Bin Faisal University P.O. Box 1982, Dammam 34212, Saudi Arabia.  
The authors declare no conflict of interest.