AMamNet: Attention-Enhanced Mamba Network for Hyperspectral Remote Sensing Image Classification

Abstract

Hyperspectral imaging, a key technology in remote sensing, captures rich spectral information beyond the visible spectrum, rendering it indispensable for advanced classification tasks. However, with developments in hyperspectral imaging, spatial–spectral redundancy and spectral confusion have increasingly revealed the limitations of convolutional neural networks (CNNs) and vision transformers (ViT). Recent advancements in state space models (SSMs) have demonstrated their superiority in linear modeling compared to convolution and transformer-based approaches. Based on this foundation, this study proposes a model named AMamNet that integrates convolutional and attention mechanisms with SSMs. As a core component of AMamNet, Attention-Bidirectional Mamba Block, leverages the self-attention mechanism to capture inter-spectral dependencies, while SSMs enhance sequential feature extraction, effectively managing the continuous nature of hyperspectral image spectral bands. Technically, a multi-scale convolution stem block is designed to achieve shallow spatial–spectral feature fusion and reduce information redundancy. Extensive experiments conducted on three benchmark datasets, namely the Indian Pines dataset, Pavia University dataset, and WHU-Hi-LongKou dataset, demonstrate that AMamNet achieves robust, state-of-the-art performance, underscoring its effectiveness in mitigating redundancy and confusion within the spatial–spectral characteristics of hyperspectral images.

Keywords

• hyperspectral image
• classification
• remote sensing
• mamba
• transformer