LMamba: Local-Guided Mamba with Multi-Scale Filtering for Hyperspectral Image Classification.
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| Title: | LMamba: Local-Guided Mamba with Multi-Scale Filtering for Hyperspectral Image Classification. |
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| Authors: | Yang, Xiaofei1 (AUTHOR), Wei, Yao1 (AUTHOR), Tan, Jiarong1 (AUTHOR), Li, Shuqi1 (AUTHOR) surkyli@gzhu.edu.cn, Tang, Haojin1 (AUTHOR), Liu, Waixi1 (AUTHOR) |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 10, p1629. 22p. |
| Subjects: | State-space methods, Spatial arrangement, Optimization algorithms, Image recognition (Computer vision), Feature extraction, Deep learning |
| Abstract: | Highlights: What are the main findings? LMamba achieves state-of-the-art classification performance across three established public hyperspectral image (HSI) benchmarks. The Multi-scale Aggregation and Compression Block (MACB) and the Locally Guided 2D Scanning Mechanism exhibit a synergistic effect in mitigating spectral redundancy and preserving spatial structural integrity. The LMamba framework maintains superior computational efficiency relative to its high accuracy. What is the implication of the main finding? It establishes a novel and efficient architectural paradigm for hyperspectral image classification. It underscores the critical necessity of preserving two-dimensional geometric priors within sequential state space models for visual tasks. Deep learning methods have significantly improved hyperspectral image (HSI) classification by exploiting hierarchical feature learning to integrate spatial and spectral information, thus significantly improving classification accuracy. Nevertheless, current deep learning approaches (such as CNNs, Transformers and Mamba) still face three major challenges: inadequate mitigation of spectral redundancy, high computational costs associated with global modeling, and the loss of two-dimensional spatial structure during sequential processing. To address these issues, we propose LMamba, a task-oriented hybrid framework that combines multi-scale convolutional filtering with local-context-conditioned state space modeling for hyperspectral image classification. Rather than introducing a fundamentally new SSM formulation, LMamba focuses on adapting the input-dependent parameter projection of Mamba to HSI data by injecting local 2D neighborhood context into the generation of selective SSM parameters. This design enables the state space module to better preserve spatial continuity while maintaining linear-complexity sequence modeling. The framework consists of two core components. First, the Multi-scale Aggregation and Compression Block (MACB) employs parallel grouped convolutions with varying kernel sizes to capture spatial features at multiple scales while simultaneously reducing spectral redundancy through channel compression. Second, the Locally Guided 2D Scanning Mechanism replaces conventional unidirectional 1D scanning with a context-aware 2D scanning strategy, thereby preserving structural continuity and enhancing feature representation by integrating local neighborhood spatial information into state transitions. Validation on three prominent HSI datasets demonstrates that LMamba consistently outperforms state-of-the-art methods based on CNNs, Transformers, and SSMs as measured by overall accuracy (OA), average accuracy (AA), and the Kappa coefficient. In summary, LMamba provides an efficient and accurate HSI classification framework under the considered benchmark settings, and its compact complexity and low-sample robustness suggest potential usefulness for practical HSI analysis. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? LMamba achieves state-of-the-art classification performance across three established public hyperspectral image (HSI) benchmarks. The Multi-scale Aggregation and Compression Block (MACB) and the Locally Guided 2D Scanning Mechanism exhibit a synergistic effect in mitigating spectral redundancy and preserving spatial structural integrity. The LMamba framework maintains superior computational efficiency relative to its high accuracy. What is the implication of the main finding? It establishes a novel and efficient architectural paradigm for hyperspectral image classification. It underscores the critical necessity of preserving two-dimensional geometric priors within sequential state space models for visual tasks. Deep learning methods have significantly improved hyperspectral image (HSI) classification by exploiting hierarchical feature learning to integrate spatial and spectral information, thus significantly improving classification accuracy. Nevertheless, current deep learning approaches (such as CNNs, Transformers and Mamba) still face three major challenges: inadequate mitigation of spectral redundancy, high computational costs associated with global modeling, and the loss of two-dimensional spatial structure during sequential processing. To address these issues, we propose LMamba, a task-oriented hybrid framework that combines multi-scale convolutional filtering with local-context-conditioned state space modeling for hyperspectral image classification. Rather than introducing a fundamentally new SSM formulation, LMamba focuses on adapting the input-dependent parameter projection of Mamba to HSI data by injecting local 2D neighborhood context into the generation of selective SSM parameters. This design enables the state space module to better preserve spatial continuity while maintaining linear-complexity sequence modeling. The framework consists of two core components. First, the Multi-scale Aggregation and Compression Block (MACB) employs parallel grouped convolutions with varying kernel sizes to capture spatial features at multiple scales while simultaneously reducing spectral redundancy through channel compression. Second, the Locally Guided 2D Scanning Mechanism replaces conventional unidirectional 1D scanning with a context-aware 2D scanning strategy, thereby preserving structural continuity and enhancing feature representation by integrating local neighborhood spatial information into state transitions. Validation on three prominent HSI datasets demonstrates that LMamba consistently outperforms state-of-the-art methods based on CNNs, Transformers, and SSMs as measured by overall accuracy (OA), average accuracy (AA), and the Kappa coefficient. In summary, LMamba provides an efficient and accurate HSI classification framework under the considered benchmark settings, and its compact complexity and low-sample robustness suggest potential usefulness for practical HSI analysis. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18101629 |
