LightMamba: A Lightweight Mamba Network for the Joint Classification of HSI and LiDAR Data

LIAO Diling; LAI Tao; HUANG Haifeng; WANG Qingsong

doi:10.11999/JEIT250981

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LIAO Diling, LAI Tao, HUANG Haifeng, WANG Qingsong. LightMamba: A Lightweight Mamba Network for the Joint Classification of HSI and LiDAR Data[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250981

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LIAO Diling, LAI Tao, HUANG Haifeng, WANG Qingsong. LightMamba: A Lightweight Mamba Network for the Joint Classification of HSI and LiDAR Data[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250981

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LightMamba: A Lightweight Mamba Network for the Joint Classification of HSI and LiDAR Data

doi: 10.11999/JEIT250981 cstr: 32379.14.JEIT250981

College of Electronic and Communication Engineering, Sun Yat-sen University, Shenzhen 518107, China

Funds: The National Natural Science Foundation of China (62273365), Xiaomi Young Talents Program

Received Date: 2025-09-25
Accepted Date: 2025-12-30
Rev Recd Date: 2025-12-30

Available Online: 2026-01-09

Abstract

Abstract

Objective The joint classification of HyperSpectral Imagery (HSI) and Light Detection And Ranging (LiDAR) data is a critical task in remote sensing, where complementary spectral and spatial information is exploited to improve land cover recognition accuracy. However, mainstream deep learning approaches, particularly those based on Convolutional Neural Networks (CNNs) and Transformers, are constrained by high computational cost and limited efficiency in modeling long-range dependencies. CNN-based methods are effective for local feature extraction but suffer from limited receptive fields and increased parameter counts when scaled. Transformer architectures provide global context modeling but incur quadratic computational complexity due to self-attention mechanisms, which leads to prohibitive costs when processing high-dimensional remote sensing data. To address these limitations, a lightweight network architecture named LightMamba is proposed. The model leverages an advanced State Space Model (SSM) to achieve efficient and accurate joint classification of HSI and LiDAR data. The objective is to maintain linear computational complexity while effectively fusing multi-source features and capturing global contextual relationships, thereby supporting resource-constrained applications without accuracy degradation. Methods The proposed LightMamba framework consists of three core components. First, a MultiSource Alignment Module (MSAM) is designed to address heterogeneity between HSI and LiDAR data. A dual-branch network with shared weights projects both modalities into a unified feature space, which ensures consistent spatial-spectral representation. This shared-weight strategy reduces the parameter count and strengthens inter-modal correlation through the learning of common foundational features. Second, the Multi-Source Lightweight Mamba Module (MSLMM) forms the core of the framework. Aligned HSI and LiDAR feature sequences are processed using a parameter-efficient Mamba architecture. A hybrid parameter-sharing strategy is adopted by combining shared matrices with modality-specific parameters, which preserves discriminative capability while reducing redundancy. LiDAR elevation information is used as a positional guide to enhance spatial awareness during feature fusion. The selective scanning mechanism of the SSM enables efficient modeling of long-range dependencies with linear complexity, thereby avoiding the quadratic cost associated with Transformers. Spectral bands are processed sequentially to preserve joint spectral spatial characteristics. Finally, a MultiLayer Perceptron (MLP)-based classifier maps fused high-level features to class probabilities with low computational overhead. The model is trained end to end using cross-entropy loss. Evaluations are conducted on two public benchmarks, namely the Houston and Augsburg datasets. Comparisons are performed against representative methods, including CoupledCNN, GAMF, HCT, MFT, Cross-HL, and S2CrossMamba, using Overall Accuracy (OA), Average Accuracy (AA), and the Kappa coefficient. Ablation experiments analyze the contribution of each module, and parameter count and FLoating-Point Operations (FLOPs) are reported. Results and Discussions Experimental results demonstrate that LightMamba achieves superior performance and efficiency. On the Houston dataset, an OA of 94.30%, an AA of 95.25%, and a Kappa coefficient of 93.83% are obtained, which exceed those of all comparison methods. Perfect classification accuracy is achieved for several classes, including Soil and Water. Classification maps exhibit improved spatial continuity and internal consistency, with reduced speckle noise, particularly in heterogeneous regions such as commercial areas. On the Augsburg dataset, LightMamba achieves the highest OA of 87.41% and a Kappa coefficient of 82.30%, which confirms strong generalization across different scenes. Although the AA is slightly lower than that of S2CrossMamba, the higher OA and Kappa values indicate better overall performance. Complexity analysis shows that LightMamba attains high accuracy with a lightweight structure containing only 69.93 k parameters, which is substantially fewer than GAMF and comparable to S2CrossMamba, while maintaining moderate FLOPs. Experiments on input patch size indicate adaptability to scene characteristics, with optimal performance observed at 17×17 for the Houston dataset and 9×9 for the Augsburg dataset. Conclusions A lightweight network architecture, LightMamba, is presented for joint HSI and LiDAR classification. By combining a shared-weight MSAM with a lightweight Mamba module that adopts hybrid parameterization and elevation-guided fusion, modal heterogeneity is effectively addressed and long-range contextual dependencies are captured with linear computational complexity. Experimental results on public benchmarks demonstrate state-of-the-art classification accuracy with a reduced parameter count and computational cost compared with existing methods. These findings confirm the potential of Mamba-based architectures for efficient multi-source remote sensing data fusion. Future research will explore optimized two-dimensional scanning mechanisms and adaptive scanning strategies to further improve feature capture efficiency and classification performance. The LightMamba code is available at https://www.scidb.cn/detail?dataSetId=064dc4ac5350418e87a8b82dd324737b&version=V1&code=j00173.
- Hyperspectral image classification,
- Multi-source data fusion,
- State space model,
- Lightweight network,
- Mamba

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References(25)

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