## 3 Experiments and Results
### Experimental Results  
Four real HSI data sets were used to verify the feasibility of the object-oriented hyperspectral color visualization approach proposed in this paper. Five kinds of manifold algorithms, namely, T-SNE, LE, ISOMAP, LLE, and LTSA, were used for dimension reduction of the proposed method (Section 2.2) to obtain the object-oriented output images. These output images were also compared with traditional data-oriented visualization methods, such as principal component analysis (PCA), fixed linear spectral weighting envelopes (the color matching function (CMF) is the generally used weighting function), and band selection based on optimal basis fitting (referred to as BS). All the experiments were described in HSV color space, without a preset color. The hue class values were selected perspectives around the central axis of the cone. The initial hue phase was \(a\ =\ 0\). The separation factor was \(r\ =\ 0\). The display results for each data set and the color display image for the data-oriented method using PCA, CMF, and BS can be seen in subfigures (a) to (c) in Figures 5-8. Furthermore, subfigures (d) to (h) in Figures 5-8 display the visualization results of the proposed object-oriented method using the manifold learning method. As seen in Figures 5-8, the proposed object-oriented methods (subfigures (d) to (h) in Figures 5-8) were in general visually better in terms of separability of the classes and showed more colorful and vivid ground objects in the images. In this approach, the different classes were, in most cases, displayed clearly (e.g., the ground object classes in the dotted box in Figures 5 and 7), and the pixels of the same class were usually displayed distinctly (e.g., the pixels in the dotted box in Figures 6 and 8). Therefore, the output images for the proposed class-oriented method looked more brightly colored and also had a better eye color perception distance between the classes. Actually, the proposed object-oriented method can generate totally different colors for categories with a similar spectral response. This property directly affects the observers' ability to distinguish between the different categories. As the display results for each data set make full use of the known information, the object-oriented method has more advantages in visually distinguishing different categories. Figure 5: _Cont._  
Figure 5: Visualization result for Indian Pines. (**a**) Principal component analysis (PCA); (**b**) color matching function (CMF); (**c**) band selection; (**d**) t-distributed stochastic neighbor embedding (T-SNE); (**e**) local tangent space alignment (LTSA); (**f**) locally linear embedding (LLE); (**g**) Laplacian eigenmaps (LE); (**h**) isometric feature mapping (ISOMAP). Figure 6: Visualization result for Pavia University. (**a**) PCA; (**b**) CMF; (**c**) band selection; (**d**) T-SNE; (**e**) LTSA; (**f**) LLE; (**g**) LE; (**h**) ISOMAP. Figure 7: _Cont._  
The pixel separability of the classes in the output images, which were generated by the proposed object-oriented visualization method, was higher than for most data-oriented approaches. For example, for the three manifold algorithms, i.e., T-SNE, LE, and ISOMAP (subfigures (d), (g), and (h) in Figures 5-8), the detailed pixel display effect within the class in the output image was clearer than for the other methods. Thus, these three methods are more suitable for displaying the difference between pixels within the same class. The methods LTSA and LLE (subfigures (e) and (f) in Figures 5-8) were weaker than the other methods in terms of displaying the abnormal pixels within the classes. They are more focused on abnormal indications of pixels within classes, and therefore these methods are suitable for occasional special needs. To describe the visual effect of the various methods more objectively, four evaluation parameters were introduced into the experiment: the amount of information (OIF), correlation coefficient \(\rho\), separability \(\delta\), and average separability between classes \(\lambda\).