# A Novel Technique for Semantic Segmentation of Hyperspectral Images Using Multi-View Features
###### Abstract  
This research presents an innovative technique for semantic segmentation of Hyperspectral Image (HSI) while focusing on its dimensionality reduction. A unique technique is applied to three distinct HSI landcover datasets, Indian Pines, Pavia University, and Salinas Valley, acquired from diverse sensors. HSIs are inherently multi-view structures, causing redundancy and computation overload due to their high dimensionality. The technique utilizes Canonical Correlation Analysis (CCA) variants, Pairwise CCA (PCCA) and Multiple Set CCA (MCCA), to extract features from multiple views of the input image simultaneously. The performance of PCCA and MCCA is compared with the traditional Principal Component Analysis (PCA) on all datasets. The superior performance of CCA variants, particularly MCCA, is demonstrated in achieving higher Overall Accuracy (OA) for semantic segmentation compared to PCA. The research extends the analysis by integrating machine learning classifiers for per pixel prediction, demonstrating the effectiveness of the proposed techniques i.e., PCCA-SVM and MCCA-SVM. hyperspectral image; multi-view learning; semantic segmentation; canonical correlation analysis; principal component analysis; curse of dimensionality  
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for autonomous vehicles [8] and geolocalization for Unmanned Aerial Vehicles [9]. Environmental monitoring [10], crop cover and type analysis [11], and land usage analysis in urban areas [12] are a few instances of semantic segmentation of remote sensing imagery. However, semantic segmentation becomes particularly challenging due to the high-dimensional nature of the data, where each pixel is represented by a spectrum across numerous spectral bands [13]. The spectral bands are hundreds in number, redundant and cause [PERSON]'s curse of dimensionality [14] which necessitates efficient techniques that can capture the relevant information while handling the risk of overfitting and computational inefficiency. To deal with the Curse of Dimensionality, a bunch of Feature Selection (FS) and Feature Extraction (FE) techniques exist. While FS picks from raw features it also loses some of the original features, whereas FE converts the high dimension into meaningful low-dimensional feature space without losing any important feature. FE techniques like Principal Component Analysis (PCA) [15], Independent Component Annlysis (ICA) [16], Linear Discriminant Analysis (LDA) [17], Wavelet Transform [18] and more have been implemented on HSI. While some adopted Bilateral filter based feature extraction on Superpixels of HSI [19], others utilised adaptive variation filtering over the principal components acquired through PCA [19]. Minimum Noise Fraction has also played a vital role to reduce dimensions on superpixels of HSI to extract reduced spectral and spatial features [20]. The most important and relevant spectral bands are also found by utilizing a variety of metaheuristic optimization techniques, including Particle Swarm Optimization (PSO) [21], Genetic Algorithm (GA) [22], Gray Wolf Optimization (GWO) [23], and Whale Optimization Algorithm (WOA) [24]. Ranking-based [25] and clustering-based [26] techniques have been explored as well to reduce the dimensions. The aforementioned dimension reduction techniques focus on the global features and consider HSI as a single dataset and give a singular reduced spectral dataset. However, a Novel perspective emerges when we consider hyperspectral images as a multi-view dataset. In traditional multi-view learning scenarios, multiple sets of features or views of the same data are available. The term 'views' refers to the different ways in which the data is observed, captured, or represented. These views provide complementary information that can enhance the learning process and lead to more robust models. Recent researches have focused on multi-view feature learning and their fusion along with supremacy of Support Vector Machine (SVM).