## 4 Discussion  
In this paper, an object-oriented visualization method based on manifold methods is proposed for HSI where supervised information is available. Five kinds of manifold algorithms and four HSI data sets were used to verify the feasibility of the proposed approach. The experiments highlighted the effectiveness of the proposed method through subjective and objective evaluations. In subjective evaluations, three manifold algorithms, namely, T-SNE, LE, and ISOMAP, demonstrated sharper detailed pixel display effect within the individual classes in the output images. Thus, these three methods are more suitable for displaying the difference between pixels within the same class. Regarding objective evaluation results, the two manifold algorithms used, i.e., T-SNE and LE, showed clarity of information (OIF), good correlation (\(\rho\)) and improved pixel separability (\(\delta\)) compared to LTSA and LLE.  
In contrast to data-oriented processing, where the whole HSI data set with no supervised information is used, the object-oriented visualization method considers the observers' demands for the HSI displayed with a sense of purpose. Considering the available class information, the proposed method separately processes data from different classes and displays all the classes in the image in an independent fashion. The output images make full use of the supervised information, and an improved visualization is consequently obtained. In addition, the proposed method will play an active role in pest monitoring, disaster early warning, and automatic management of agriculture and forestry.  
Furthermore, by adjusting the separation factor, both separability between classes and within classes can be adjusted according to the demands of the observers.  
In order to adapt to the nonlinear characteristics of HSI data, manifold algorithms were used in this study. Replacing manifold algorithms with other data processing methods may still work, but the processing method needs to be chosen according to the demand of observers. By comparing experimental results from five commonly used manifold algorithms, it was observed that T-SNE and LE algorithms are superior to other algorithms, but all of them are time-consuming. Therefore, they are unsuitable for real-time processing or processing that has requirements regarding the amount of time needed. Considering the computational load, if no special requirements are needed, the LE algorithm shows the best performance.  
Figure 11: Indian Pines image display results under six different \(r\) values. (**a**) \(r=0\); (**b**) \(r=0.0038\); (**c**) \(r=0.0077\); (**d**) \(r=0.0115\); (**e**) \(r=0.0154\); (**f**) \(r=0.0183\).  
**Author Contributions:** Conceptualization, methodology, and writing--original draft preparation, D.L.; writing--review and editing, L.W. and J.A.B. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China, grant numbers 61275010 and 61675051.  
**Acknowledgments:** The authors would like to thank [PERSON] from Purdue University for providing the AVIRIS Indian Pines data set and Prof. [PERSON] from the University of Pavia for providing the ROSIS-3 University of Pavia data set. The authors would like to express their appreciation to [PERSON] from the University of Iceland and [PERSON] for helping improve the language of the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.