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MambaLIC: State-Space Models for Efficient Remote Sensing Image Compression.

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Title: MambaLIC: State-Space Models for Efficient Remote Sensing Image Compression.
Authors: Xiong, Haobo1 (AUTHOR), Liu, Kai1,2 (AUTHOR) kailiu@mail.xidian.edu.cn, Xiao, Huachao1,2 (AUTHOR), Ding, Chongyang1,2 (AUTHOR), Wang, Feiyang1 (AUTHOR)
Source: Remote Sensing. Mar2026, Vol. 18 Issue 6, p881. 30p.
Subjects: Image compression, State-space methods, Convolutional neural networks, Remote sensing, Mathematical optimization, High resolution imaging, Machine learning
Abstract: Highlights: What are the main findings? The proposed MambaLIC framework integrates the SSM module with dynamic convolution. This method facilitates the effective interaction between local and global information, thereby enhancing compression performance. Experimental results on three remote sensing image datasets (UC-Merced, LoveDA, xView) demonstrate that MambaLIC outperforms VVC (VTM-17.0) with BD-rate improvements of 14.22%, 18.48%, and 17.47%, respectively. Additionally, MambaLIC achieves superior computational efficiency and significantly lower memory consumption compared to existing Mamba-based methods. What are the implications of the main findings? MambaLIC provides an efficient architectural design for remote sensing image compression and is particularly suitable for handling high-resolution remote sensing images. By reducing memory consumption and computational complexity, MambaLIC achieves superior performance and resource utilization compared to current methods. The framework offers new insights into the field of remote sensing image compression and demonstrates the potential of SSMs in image compression, especially in handling large-scale, high-resolution images. Remote sensing (RS) images, characterized by their large size and rich texture, require algorithms capable of effectively integrating both global and local features for compression. However, existing Learned Image Compression (LIC) approaches face distinct bottlenecks. While Transformer-based architectures typically suffer from heavy computational loads, standard State Space Models (SSMs) often incur prohibitive memory costs when processing high-resolution inputs. To address these limitations, we propose MambaLIC, a novel RS image compression network that integrates the efficient long-range modeling of SSMs with the local modeling ability of CNNs. In this paper, we introduce an innovative Remote Sensing State Space Model (RS-SSM) module, which combines visual SSM with dynamic convolution for remote sensing image compression. This integration facilitates effective interaction between local and global information, thereby enhancing the performance of RS image compression. Furthermore, we propose an SSM attention-based (SSA-based) spatial-channel context model for better entropy modeling. Compared to Transformer-CNN mixed architectures, MambaLIC reduces computational complexity by 63.9% and achieves superior rate-distortion (RD) performance. Consequently, compared to the latest SS2D-based method MambaIC, MambaLIC achieves substantial efficiency gains, saving 78.8% in memory usage. Experimental results demonstrate that MambaLIC achieves state-of-the-art (SOTA) performance, outperforming VVC (VTM-17.0) by 14.22%, 18.48%, and 17.47% in BD-rate on UC-Merced, LoveDA, and xView datasets, respectively. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: MambaLIC: State-Space Models for Efficient Remote Sensing Image Compression.
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  Data: <searchLink fieldCode="DE" term="%22Image+compression%22">Image compression</searchLink><br /><searchLink fieldCode="DE" term="%22State-space+methods%22">State-space methods</searchLink><br /><searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22High+resolution+imaging%22">High resolution imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink>
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  Data: Highlights: What are the main findings? The proposed MambaLIC framework integrates the SSM module with dynamic convolution. This method facilitates the effective interaction between local and global information, thereby enhancing compression performance. Experimental results on three remote sensing image datasets (UC-Merced, LoveDA, xView) demonstrate that MambaLIC outperforms VVC (VTM-17.0) with BD-rate improvements of 14.22%, 18.48%, and 17.47%, respectively. Additionally, MambaLIC achieves superior computational efficiency and significantly lower memory consumption compared to existing Mamba-based methods. What are the implications of the main findings? MambaLIC provides an efficient architectural design for remote sensing image compression and is particularly suitable for handling high-resolution remote sensing images. By reducing memory consumption and computational complexity, MambaLIC achieves superior performance and resource utilization compared to current methods. The framework offers new insights into the field of remote sensing image compression and demonstrates the potential of SSMs in image compression, especially in handling large-scale, high-resolution images. Remote sensing (RS) images, characterized by their large size and rich texture, require algorithms capable of effectively integrating both global and local features for compression. However, existing Learned Image Compression (LIC) approaches face distinct bottlenecks. While Transformer-based architectures typically suffer from heavy computational loads, standard State Space Models (SSMs) often incur prohibitive memory costs when processing high-resolution inputs. To address these limitations, we propose MambaLIC, a novel RS image compression network that integrates the efficient long-range modeling of SSMs with the local modeling ability of CNNs. In this paper, we introduce an innovative Remote Sensing State Space Model (RS-SSM) module, which combines visual SSM with dynamic convolution for remote sensing image compression. This integration facilitates effective interaction between local and global information, thereby enhancing the performance of RS image compression. Furthermore, we propose an SSM attention-based (SSA-based) spatial-channel context model for better entropy modeling. Compared to Transformer-CNN mixed architectures, MambaLIC reduces computational complexity by 63.9% and achieves superior rate-distortion (RD) performance. Consequently, compared to the latest SS2D-based method MambaIC, MambaLIC achieves substantial efficiency gains, saving 78.8% in memory usage. Experimental results demonstrate that MambaLIC achieves state-of-the-art (SOTA) performance, outperforming VVC (VTM-17.0) by 14.22%, 18.48%, and 17.47% in BD-rate on UC-Merced, LoveDA, and xView datasets, respectively. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3390/rs18060881
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        Text: English
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        PageCount: 30
        StartPage: 881
    Subjects:
      – SubjectFull: Image compression
        Type: general
      – SubjectFull: State-space methods
        Type: general
      – SubjectFull: Convolutional neural networks
        Type: general
      – SubjectFull: Remote sensing
        Type: general
      – SubjectFull: Mathematical optimization
        Type: general
      – SubjectFull: High resolution imaging
        Type: general
      – SubjectFull: Machine learning
        Type: general
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      – TitleFull: MambaLIC: State-Space Models for Efficient Remote Sensing Image Compression.
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            NameFull: Xiong, Haobo
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            NameFull: Xiao, Huachao
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              M: 03
              Text: Mar2026
              Type: published
              Y: 2026
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