## 4 Datasets and Experimental Results
### Comparison to Baselines  
In this subsection, we compare our proposed SF-ICNN strategy with several baseline techniques, including spectral SVM (S-SVM), spectral-fractal SVM (SF-SVM), conventional spectral CNN (S-CNN), and conventional spectral-fractal CNN (SF-CNN). More information about these methods is as follows:  
* S-SVM: In this method, spectral features are classified using kernel SVM, a commonly employed classifier in the literature. * SF-SVM: In this method, stacked spectral-fractal features are classified using kernel SVM. * S-CNN: In this method, spectral features are classified using the conventional CNN approach without iteration loops. Figure 4: The PU dataset. * SF-CNN: In this method, stacked spectral-fractal features are classified using the conventional CNN approach without iteration loops. The classification accuracies of each method on the test samples are presented in Tables 3 and 4 for each dataset. The important findings of these tables are as follows:  
* Comparing the OA and kappa of S-SVM and SF-SVM demonstrates that adding spatial FFs to spectral-based SVM can significantly improve the classification accuracy. This confirms the findings of previous studies about the efficacy of spatial features in enhancing the HSI classification outcomes. * The results show that CNN-based methods outperform SVM-based methods in terms of the classification performance. This is due to the CNN's ability to consider spatial features during the classification procedures. However, the conclusion about the comparison of SF-SVM and S-CNN is mixed. In the IP dataset, SF-SVM performs better than S-CNN, but in the PU dataset, S-CNN performs better than SF-SVM. This may be because of the underlying complex land cover present in each scene. * A comparison of the S-CNN and SF-CNN outcomes for the two datasets shows that although the CNN can take into account the spatial properties of pixels, its performance can still be enhanced by applying manually created FFs as inputs. * Comparison of SF-ICNN and other techniques demonstrates that our proposed strategy outperforms all the baselines. Also, the results show that the proposed iterative strategy is efficient since SF-ICNN reaches higher classification accuracies than SF-CNN. * The best baseline approach, SF-CNN, is compared with the suggested SF-ICNN method using the Z-score test [46]. The Z-score is a statistical measure used to compare the performance of two classification methods from a statistical point of view. The Z-score values between the SF-CNN approach (the best baseline method) and the SF-ICNN approach exceeded 1.96 for both datasets (2.87 for IP and 2.95 for PU), indicating significant accuracy improvements. * In the context of class accuracies, the proposed SF-ICNN outperforms the other method in 15 of 16 classes for the IP dataset and 6 of 9 classes for the PU dataset. This further demonstrated the suggested method's effectiveness in raising the classification accuracy across the majority of classes. * Figures 5 and 6 show the GT images and the final classification images of each classification method. As can be seen from the figures, our proposed SF-ICNN method shows a significant reduction in misclassified pixels in the classified images compared to the baseline methods. Figure 5: GT and other techniques’ classification results for the IP HSI dataset. (**a**) GT. (**b**) S-SVM. (**c**) SF-SVM. (**d**) S-CNN. (**e**) SF-CNN. (**f**) Suggested SF-ICNN.