# A Novel Technique for Semantic Segmentation of Hyperspectral Images Using Multi-View Features
## 4 Proposed Technique
### Semantic Segmentation Using Support Vector Machine  
SVM is a powerful machine learning algorithm commonly used for classification tasks. In this paper, we focus on two fundamental tasks: per-pixel prediction and semantic segmentation. Per-pixel prediction involves the classification of each individual pixel  
Figure 5: Overview of the Proposed Technique for Semantic Segmentation of HSI. (The same image scene captured at different spectral bands is shown as views with different colors).  
within an HSI dataset into specific land-cover categories. Semantic segmentation, on the other hand, extends this concept by partitioning the entire image into coherent regions, each corresponding to a distinct class or category. The algorithm begins by splitting the dataset into training and testing sets to evaluate the model's performance. To ensure consistent and meaningful comparisons, the feature matrix obtained from CCA is normalized to have zero mean and unit variance. This step is crucial for preventing certain features from dominating the learning process due to their larger scales.  
The selection of an appropriate kernel function and regularization parameter, \(C\) is a pivotal aspect of SVM which has been selected through Grid Search Optimization. The kernel computes the similarity between data points in the transformed space, where the choice of the kernel bandwidth parameter, \(\sigma\), is crucial in capturing the relationships between the features. The SVM model is trained using the normalized feature matrix, incorporating the kernel matrix that quantifies the pairwise similarities between data points. \(C\) balances the trade-off between achieving a low training error and a simple model, helping prevent overfitting.  
Once trained, the SVM model is capable of predicting the class labels of new data points. This is achieved by computing the decision function, which evaluates the weighted sum of the Lagrange multipliers (\(\alpha_{i}\)) and the corresponding labels (\(y_{i}\)) for the support vectors. The model's performance is then assessed on the testing set, using standard metrics such as accuracy. Overall, SVM is a versatile and effective algorithm for semantic segmentation tasks, providing a robust solution when combined with feature reduction techniques like CCA.  
The Proposed Technique of extracting features from the multi-view dataset and performing Semantic Segmentation using SVM has been discussed in Algorithm 1.  
```
0: Hyperspectral image dataset \(X_{n}^{m}\) with \(n\) pixel samples and \(m\) spectral bands, Number of Subsets/Views \(K\).
0: Predicted Classes of each Pixel \(C=1,2,3\ldots C_{i}\).
1:begin
2: Divide the hyperspectral image dataset \(X\) into \(K\) subsets, each containing adjacent spectral bands. Denote these subsets as \(\{X_{i}\}_{i=1}^{K}\).
3:for\(j\) from 3 to 10 do
4: Standardize the data in every \(X_{i}\) to have zero mean and unit variance.
5: Perform CCA to extract Canonical Variables \(U_{j}\).
6: Combine the least correlated \(U_{j}\) to form a feature matrix \(U\).
7:endfor
8: Hyper-tune the different Parameters of SVM like kernel function, \(C\) and \(gamma\) using Grid Search Optimization.
9: Compute the kernel matrix \(M\) based on the chosen best values of kernel function, \(C\) and \(gamma\).
10: Train the SVM classifier on \(U\).
11: Predict the class labels for each pixel in the hyperspectral image using the trained SVM model.
12:end
```  
**Algorithm 1** Semantic Segmentation using Multi-View Features Algorithm