Hybrid State-Space and GRU-based Graph Tokenization Mamba for Hyperspectral Image Classification
Journal:
arXiv
Published Date:
Feb 10, 2025
Abstract
Hyperspectral image (HSI) classification plays a pivotal role in domains such
as environmental monitoring, agriculture, and urban planning. However, it faces
significant challenges due to the high-dimensional nature of the data and the
complex spectral-spatial relationships inherent in HSI. Traditional methods,
including conventional machine learning and convolutional neural networks
(CNNs), often struggle to effectively capture these intricate spectral-spatial
features and global contextual information. Transformer-based models, while
powerful in capturing long-range dependencies, often demand substantial
computational resources, posing challenges in scenarios where labeled datasets
are limited, as is commonly seen in HSI applications. To overcome these
challenges, this work proposes GraphMamba, a hybrid model that combines
spectral-spatial token generation, graph-based token prioritization, and
cross-attention mechanisms. The model introduces a novel hybridization of
state-space modeling and Gated Recurrent Units (GRU), capturing both linear and
nonlinear spatial-spectral dynamics. GraphMamba enhances the ability to model
complex spatial-spectral relationships while maintaining scalability and
computational efficiency across diverse HSI datasets. Through comprehensive
experiments, we demonstrate that GraphMamba outperforms existing
state-of-the-art models, offering a scalable and robust solution for complex
HSI classification tasks.
