Remote Sensing Image Compression via Wavelet-Guided Local Structure Decoupling and Channel–Spatial State Modeling
As the resolution and data volume of remote sensing imagery continue to grow, achieving efficient compression without sacrificing reconstruction quality remains a major challenge, given that traditional handcrafted codecs often fail to balance rate-distortion performance and computational complexity...
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MDPI AG
2025-07-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/14/2419 |
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| author | Jiahui Liu Lili Zhang Xianjun Wang |
| author_facet | Jiahui Liu Lili Zhang Xianjun Wang |
| author_sort | Jiahui Liu |
| collection | DOAJ |
| description | As the resolution and data volume of remote sensing imagery continue to grow, achieving efficient compression without sacrificing reconstruction quality remains a major challenge, given that traditional handcrafted codecs often fail to balance rate-distortion performance and computational complexity, while deep learning-based approaches offer superior representational capacity. However, challenges remain in achieving a balance between fine-detail adaptation and computational efficiency. Mamba, a state–space model (SSM)-based architecture, offers linear-time complexity and excels at capturing long-range dependencies in sequences. It has been adopted in remote sensing compression tasks to model long-distance dependencies between pixels. However, despite its effectiveness in global context aggregation, Mamba’s uniform bidirectional scanning is insufficient for capturing high-frequency structures such as edges and textures. Moreover, existing visual state–space (VSS) models built upon Mamba typically treat all channels equally and lack mechanisms to dynamically focus on semantically salient spatial regions. To address these issues, we present an innovative architecture for distant sensing image compression, called the Multi-scale Channel Global Mamba Network (MGMNet). MGMNet integrates a spatial–channel dynamic weighting mechanism into the Mamba architecture, enhancing global semantic modeling while selectively emphasizing informative features. It comprises two key modules. The Wavelet Transform-guided Local Structure Decoupling (WTLS) module applies multi-scale wavelet decomposition to disentangle and separately encode low- and high-frequency components, enabling efficient parallel modeling of global contours and local textures. The Channel–Global Information Modeling (CGIM) module enhances conventional VSS by introducing a dual-path attention strategy that reweights spatial and channel information, improving the modeling of long-range dependencies and edge structures. We conducted extensive evaluations on three distinct remote sensing datasets to assess the MGMNet. The results of the investigations revealed that MGMNet outperforms the current SOTA models across various performance metrics. |
| format | Article |
| id | doaj-art-cd8c717159a8475baaf5fe6ffca81c16 |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-cd8c717159a8475baaf5fe6ffca81c162025-08-20T03:07:56ZengMDPI AGRemote Sensing2072-42922025-07-011714241910.3390/rs17142419Remote Sensing Image Compression via Wavelet-Guided Local Structure Decoupling and Channel–Spatial State ModelingJiahui Liu0Lili Zhang1Xianjun Wang2College of Electronic and Information Engineering, Shenyang Aerospace University, Shenyang 110136, ChinaCollege of Electronic and Information Engineering, Shenyang Aerospace University, Shenyang 110136, ChinaCollege of Electronic and Information Engineering, Shenyang Aerospace University, Shenyang 110136, ChinaAs the resolution and data volume of remote sensing imagery continue to grow, achieving efficient compression without sacrificing reconstruction quality remains a major challenge, given that traditional handcrafted codecs often fail to balance rate-distortion performance and computational complexity, while deep learning-based approaches offer superior representational capacity. However, challenges remain in achieving a balance between fine-detail adaptation and computational efficiency. Mamba, a state–space model (SSM)-based architecture, offers linear-time complexity and excels at capturing long-range dependencies in sequences. It has been adopted in remote sensing compression tasks to model long-distance dependencies between pixels. However, despite its effectiveness in global context aggregation, Mamba’s uniform bidirectional scanning is insufficient for capturing high-frequency structures such as edges and textures. Moreover, existing visual state–space (VSS) models built upon Mamba typically treat all channels equally and lack mechanisms to dynamically focus on semantically salient spatial regions. To address these issues, we present an innovative architecture for distant sensing image compression, called the Multi-scale Channel Global Mamba Network (MGMNet). MGMNet integrates a spatial–channel dynamic weighting mechanism into the Mamba architecture, enhancing global semantic modeling while selectively emphasizing informative features. It comprises two key modules. The Wavelet Transform-guided Local Structure Decoupling (WTLS) module applies multi-scale wavelet decomposition to disentangle and separately encode low- and high-frequency components, enabling efficient parallel modeling of global contours and local textures. The Channel–Global Information Modeling (CGIM) module enhances conventional VSS by introducing a dual-path attention strategy that reweights spatial and channel information, improving the modeling of long-range dependencies and edge structures. We conducted extensive evaluations on three distinct remote sensing datasets to assess the MGMNet. The results of the investigations revealed that MGMNet outperforms the current SOTA models across various performance metrics.https://www.mdpi.com/2072-4292/17/14/2419remote sensingimage compressionvisual mambawavelet transform-guided local structure decouplingchannel–global information modeling |
| spellingShingle | Jiahui Liu Lili Zhang Xianjun Wang Remote Sensing Image Compression via Wavelet-Guided Local Structure Decoupling and Channel–Spatial State Modeling Remote Sensing remote sensing image compression visual mamba wavelet transform-guided local structure decoupling channel–global information modeling |
| title | Remote Sensing Image Compression via Wavelet-Guided Local Structure Decoupling and Channel–Spatial State Modeling |
| title_full | Remote Sensing Image Compression via Wavelet-Guided Local Structure Decoupling and Channel–Spatial State Modeling |
| title_fullStr | Remote Sensing Image Compression via Wavelet-Guided Local Structure Decoupling and Channel–Spatial State Modeling |
| title_full_unstemmed | Remote Sensing Image Compression via Wavelet-Guided Local Structure Decoupling and Channel–Spatial State Modeling |
| title_short | Remote Sensing Image Compression via Wavelet-Guided Local Structure Decoupling and Channel–Spatial State Modeling |
| title_sort | remote sensing image compression via wavelet guided local structure decoupling and channel spatial state modeling |
| topic | remote sensing image compression visual mamba wavelet transform-guided local structure decoupling channel–global information modeling |
| url | https://www.mdpi.com/2072-4292/17/14/2419 |
| work_keys_str_mv | AT jiahuiliu remotesensingimagecompressionviawaveletguidedlocalstructuredecouplingandchannelspatialstatemodeling AT lilizhang remotesensingimagecompressionviawaveletguidedlocalstructuredecouplingandchannelspatialstatemodeling AT xianjunwang remotesensingimagecompressionviawaveletguidedlocalstructuredecouplingandchannelspatialstatemodeling |