## 5 Conclusions  
Our study introduces a new deep-learning approach for the classification of HSI. Our proposed method combines spectral features and FFs as well as PMs in an iterative manner in the CNN classifier, resulting in highly accurate classification images. We conducted experiments using the IP and PU HSI datasets. We demonstrated that our SF-ICNN strategy outperformed basic methods by about 18% and 6.4% on average for the IP and PU datasets, respectively. The statistical analysis using the Z-score confirmed the statistical significance of this improvement. Additionally, we compared our SF-ICNN approach with some recently developed spatial and spectral classification methods and found that our  
\begin{table}
\begin{tabular}{c c c} \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**OA**} \\ \cline{2-3}  & **IP** & **PU** \\ \hline [PERSON] et al. [23] * & 91.33\% & 94.65\% \\ [PERSON] et al. [32] * & 97.76\% & 94.21\% \\ [PERSON] et al. [27] ** & 95.63\% & — ** \\ [PERSON] et al. [33] ** & — & 92.23\% \\ [PERSON] et al. [34] ** & 94.07\% & — \\ [PERSON] et al. [37] ** & 98.75\% & 95.19\% \\ [PERSON] et al. [39] ** & 98.62\% & — \\ [PERSON] et al. [25] ** & 96.59\% & — \\
**Proposed SF-ICNN * & **99.16\%** & **95.5\%** \\ \hline \end{tabular}  
* The result is reported based on the implementation, ** The original paper’s outcome is reported. *** The original study does not report the outcome.  
\end{table}
Table 5: Comparison with some SOTA methods (the best results of each column are highlighted).  
method can achieve an acceptable processing time and superior classification accuracy. The proposed SF-ICNN framework also has potential applications beyond hyperspectral image classification. It can be used in various remote-sensing image analysis domains like land-cover mapping, urban planning, change detection, and environmental monitoring, where precise image classification is crucial. Even further, the proposed method can also be adopted to improve the CNN performance in texture analysis tasks such as medical image segmentation, anomaly detection, and face recognition. We intend to improve the SF-ICNN approach by making it less sensitive to the sample size by adopting a more advanced base model. Additionally, we aim to reduce the computational complexity by using advanced hardware such as GPUs and techniques such as parallel processing that increase efficiency while maintaining accuracy. These changes will enhance the performance and usability of the method.  
Conceptualization, B.A.B.; methodology, B.A.B.; software, B.A.B.; validation, B.A.B.; formal analysis, B.A.B.; writing--original draft preparation, B.A.B., M.A.P. and V.A.; writing--review and editing, B.A.B., M.A.P. and V.A.; supervision, V.A. All authors have read and agreed to the published version of the manuscript.  
This publication's funding was provided by the University of Stirling Library.  
Not applicable.  
This work investigated publicly accessible datasets, which are available at [[http://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes](http://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes)]([http://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes](http://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes)) (accessed on 4 August 2024).  
The authors declare no conflicts of interest.