Gated Graph Attention-based Crossover Snake (GGA-CS) Algorithm for Hyperspectral Image Classification

Abstract

Hyperspectral image classification involves assigning pixels or regions within a hyperspectral image to specific classes or categories based on the spectral information captured across multiple bands. Traditional method faces several challenges such as High Dimensionality, Scalability, Spectral Variability, as well as Limited Contextual Information. Hence to solve these issues a novel Gated Graph Attention-based Crossover Snake (GGA-CS) algorithm is proposed for classifying hyperspectral images. In this work, a Graph Neural Network (GNN) is employed to capture both spectral and spatial relationships between pixels, and a gated attention mechanism is utilized to enhance specific spectral bands. After the training process, a crossover-based snake optimization is applied that tuned the parameter and obtain classification output of GNN and adjust the pixels to enhance the performances of GGA-CS method. The study is validated on diverse datasets namely the Indian Pines dataset, the University of Pavia dataset, as well as Salinas dataset. The evaluation of the GGA-CS method’s performance includes assessing its effectiveness using key metrics. Comparisons with state-of-the-art methods are conducted to gauge its efficacy in hyperspectral image classification, as demonstrated by experimental results.