# A Novel Technique for Semantic Segmentation of Hyperspectral Images Using Multi-View Features
## 2 Background
With the advent of big data, CCA has helped to find patterns and associations between hundreds or even thousands of behavioural, neurological, and genetic phenotypic subject characteristics of brain image dataset in neuroscience [43]. CCA has also played a significant role in medical field by combining with deep learning techniques to segment white blood cells and classify blood samples [44]. CCA extracted various features from overlapping nuclei, compressed input images' dimensions and helped network converge faster. Along with dimension reduction, the powerful predictive power of machine learning algorithms has shown great results for the classification and segmentation of such images [45]. For the last decade, Deep Learning based techniques have been employed to perform Semantic Segmentation [46]. Some popular Convolutional Neural Network (CNN) architectures have been used on which various researchers have based their semantic segmentation. Their work is inspired from architectures of AlexNet [47], VGGNet [48] Unet [49] and GoogleNet [50]. AlexNet is made up of three fully-connected layers and five convolutional layers. A pooling layer, designed to lower dimensionality, sits between two convolutional layers that are next to each other. Three fully linked layers and several convolutional layers make up VGGNet. VGG-11 and VGG-16 are two examples of a suite of VGGNets that may be made by changing the number of convolutional layers. Three things set GoogLeNet apart from other CNN variations: it has one fully connected layer, has an inception module, and uses auxiliary classifiers during training. The inception model concatenates the filtering results with the max-pooling result after applying filters of three sizes: \(1\times 1\), \(3\times 3\), and \(5\times 5\) to the input. The things that set GoogLeNet apart from other CNN variations: it has one fully connected layer and has an inception module which concatenates the filtering results with the max-pooling result after applying filters of three sizes: \(1\times 1\), \(3\times 3\), and \(5\times 5\) to the input. Unet has been a frequently applied architecture to semantically segment HSI. It consists of an expanding path (decoder) and a contracting path (encoder) forming a symmetric U-shaped structure. The contracting path gradually reduces spatial dimensions while increasing feature depth, capturing context through the use of convolutional and max-pooling layers. In order to provide accurate localization and comprehensive segmentation outputs, the expanding path upsamples the feature maps using transposed convolutions and concatenates them with equivalent feature maps from the contracting path. Some researchers combined UNet with Transformers to extract spectral spatial features in HSI [51]. Figure 1: Overview of PCA vs. CCA. (**a**) A breakdown of major steps in PCA, (**b**) A breakdown of major steps in CCA. Despite their impressive capabilities, deep learning models come with some significant drawbacks. The limited HSI samples are challenging for these models as they are heavily reliant on large volumes of labeled data for effective training, which can be both costly and time-consuming to gather. Another issue is their tendency to overfit, particularly when the HSI dataset is small, resulting in poor generalization to new, unseen data. The training and testing samples tend to overlap which also cause overfitting. Additionally, deep learning models get computationally extensive and maintaining hyperparameters becomes challenging. Machine learning Techniques like SVM [52] become a robust choice to perform semantic segmentation on HSI due to its ability to handle high-dimensional data efficiently by finding optimal hyperplane that maximizes class separation for each pixel. SVM has been a powerful, supervised learning algorithm for HSI analysis in a variety of applications. It excels in scenarios where the data exhibits complex, nonlinear relationships and is particularly well-suited for hyperspectral images due to their high dimensionality.