# A Novel Technique for Semantic Segmentation of Hyperspectral Images Using Multi-View Features
## 2 Background  
Literature suggests for handling huge spectrum, Principal Component Analysis (PCA) has been a commonly employed technique to reduce the spectral dimension for HSI analysis in various applications. In 2002, [PERSON] and [PERSON] [31] introduced the application of statistical approach of PCA on HSI to reduce the number of spectral bands. Their study on two landcover datasets HYDICE and AVIRIS revealed that only first few Principal Components (PCs) gave about 70% correct classification rate. Their results were benchmarked for other researchers who heavily relied on first few PCs in their experiments. Even till recently, PCA stands as the most widely adopted pre-processing step for dimensionality reduction, which optimizes variance preservation and retains comprehensive spatial information on a global scale. [PERSON] et al. [32] employed PCA and a guassian kernel PCA to get informative bands. The reduced spectrum along with morphological operations, was used to classify hyperspectral images. [PERSON] et al. [33] segmented the bruiseson apple, where PCA was primarily used to remove redundant information from HSI cube. The first three PCs were used as input for the segmentation task. [PERSON] et al. [34] reduced the abundant spectral information using a combination of PCA and Analysis of Variance (ANOVA) for separating the green oranges from leaves. [PERSON] et al. [35] pointed out the complex matrix computations of eigenvectors and eigenvalues on such high dimensional data that were needed for PCA. So they introduced a reconfigurable hardware version of PCA using field-programmable gate arrays for high-speed computing of HSI. [PERSON] et al. [36] selected informative bands from HSI using fast and robust PCA on Laplacian graph. The original data could be cleanly represented in low-rank approximation due to PCA. After double PCA implementation by [PERSON] et al. [37], the third PC of HSI gave the best results for segmenting the decayed portions of apples. Due to global variance in data and the dominance of visible and near-infrared bands, PCA is unable to extract the fundamental structure of HSI, and cumulative-variance accumulation is unable to take advantage of non-linear correlations among the transformed features that PCA-based approaches provide. Hence, [PERSON] et al. [38] used Mutual Information based Minimum Redundance and Maximum Relevance measure based PCA to capture non linear features of HSI. The existing literature is saturated with a variety of combinations of PCA employed on HSI to reduce dimensions. CCA and PCA can be considered as sister techniques. However, PCA ignores the loss of information and lack of interpretation of the principal components in terms of original features. A brief abstract overview and difference between PCA and CCA has been depicted in Figure 1. Since it can reduce the labeled instance complexity, CCA, which was first developed to measure the linear correlation between two sets of variables, was formally described as a multi-view dimensionality reduction technique by [PERSON] et al. [39]. An additional attractive feature of CCA is that in the event that noise in one view is uncorrelated to the other, the learnt subspace will not contain the noise in the uncorrelated dimensions [40]. CCA is an effective approach for extracting features from multi-view images by uncovering the relationships between different sets of variables. Recent researches show it improves the robustness of classification techniques by combining the strengths of different viewpoints to create a more comprehensive representation. CCA created a hybrid and improved kernel for HSI classification by capturing similarities between spectral and spatial features and hence combining them for SVM [41]. It has also addressed limited training samples issue by performing cross-domain collaborative learning [42]. It assigns labels to unknown samples based on correlation between target clusters created using random walker algorithm.