High-dimensional image analysis, such as Hyperspectral Imaging (HSI) data, poses unique challenges due to their high dimensionality and non-Euclidean structures, making their analysis and classification complex. In this study, we explore the use of both graph deep learning (GDL) and multi-view graph representation learning for HSI classification. Furthermore, we present our proposed approach of multi-view Graph Convolutional Networks (GCNs) and how it leverages multiple views of the data by combining spectral and spatial features to improve classification accuracy. We discuss then specific challenges encountered when training our model on large HSIs, including managing large-scale graph data. We also discuss promising opportunities to overcome these challenges. By highlighting the challenges and opportunities associated with GDL and multi-view GCN usage for HSI classification, this study aims to shed light on recent developments and future prospects in this rapidly evolving field.