Hyperspectral Image Classification Using Batch-Based Graph Attentional Networks in Agriculture

Abstract

The classification of hyperspectral images plays a critical role in the maintenance of remote image analysis, which has attracted a lot of research interest. Despite the fact that numerous methodologies, including unsupervised and supervised methods, have been presented, achieving an acceptable classification result remains a challenging task. Deep learning-based hyperspectral image (HSI) classification is gaining popularity, because of its efficient classification capabilities. When compared to traditional convolutional neural networks, graph-based deep learning provides the benefits of exhibiting class boundaries and modelling feature relationships. In hyperspectral image (HSI) classification, the most important problem is how to transform hyperspectral data into irregular domains from regular grids. This study describes a method for image classification that employs graph neural network (GNN) models. The input images are converted into region adjacency graphs (RAGs), where regions are super pixels and edges link nearby super pixels.