The growth of Hyperspectral Image (HSI) analysis is due to technology advancements that enable cameras to collect hundreds of continuous spectral information of each pixel in an image. HSI classification is challenging due to the large number of redundant spectral bands, limited training samples and non-linear relationship between the collected spatial position and the spectral bands. Our survey highlights recent research in HSI classification using traditional Machine Learning techniques like kernel-based learning, Support Vector Machines, Dimension Reduction and Transform-based techniques. Our study also digs into Deep Learning (DL) techniques that involve the usage of Autoencoders, 1D, 2D and 3D-Convolutional Neural Networks to classify HSI. From the comparison, it is observed that DL-based classification techniques outperform ML-based techniques. It has also been observed that spectral-spatial HSI classification outperforms pixel-by-pixel classification because it incorporates spectral signatures and spatial domain information. The performance of ML and DL-based classification techniques has been reviewed on commonly used land cover datasets like Indian Pines, Salinas valley and Pavia University.

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.