Enhancing hyperspectral image classification with graph attention neural network

Abstract

Due to the rapid advancement of hyperspectral remote sensing technology, classification methods based on hyperspectral images (HSIs) have gained increasing significance in the processes of target identification, mineral mapping, and environmental management. This importance arises from the fact that HSIs offer a more comprehensive understanding of a target's composition. However, addressing the challenges posed by the high dimensionality and redundancy of HSI sets, coupled with potential class imbalances in hyperspectral datasets, remains a complex task. Both convolutional neural networks (CNNs) and graph convolutional networks (GCNs) have demonstrated promising results in HSI classification in recent years. Nonetheless, CNNs struggle to attain high accuracy with limited sample sizes, whereas GCNs demand substantial computational resources. Oversmoothing remains a persistent challenge with conventional GCNs. In response to these issues, an approach known as the graph attention neural network for remote target classification (GATN-RTC) has been proposed. GATN-RTC employs a spectral filter and an autoregressive moving average filter to classify distant targets, addressing datasets both with and without labeled samples. To evaluate the performance of GATN-RTC, we conducted a comparative analysis against state-of-the-art methodologies using key performance metrics, such as overall accuracy (OA), per-class accuracy, and the Cohen's Kappa statistic (KC). The findings reveal that GATN-RTC outperforms existing approaches, achieving improvements of 5.95% in OA, 5.33% in per-class accuracy, and 8.28% in the Cohen's KC for the Salinas dataset. Furthermore, it demonstrates enhancements of 6.05% and 6.4% in OA, 6.56% and 5.89% in per-class accuracy, and 6.71% and 6.23% in the Cohen's KC for the Pavia University dataset.