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State-Space Model Meets Linear Attention: A Hybrid Architecture for Internal Wave Segmentation.

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Title: State-Space Model Meets Linear Attention: A Hybrid Architecture for Internal Wave Segmentation.
Authors: An, Zhijie1 (AUTHOR), Li, Zhao1,2 (AUTHOR), Barintag, Saheya1 (AUTHOR), Zhao, Hongyu1,2 (AUTHOR), Yao, Yanqing2 (AUTHOR), Jiao, Licheng1 (AUTHOR), Gong, Maoguo1 (AUTHOR) gong@ieee.org
Source: Remote Sensing. Sep2025, Vol. 17 Issue 17, p2969. 18p.
Subjects: Internal waves, Image segmentation, Feature extraction, Remote sensing, Remote sensing by radar, State-space methods
Abstract: Internal waves (IWs) play a crucial role in the transport of energy and matter within the ocean while also posing significant risks to marine engineering, navigation, and underwater communication systems. Consequently, effective segmentation methods are essential for mitigating their adverse impacts and minimizing associated hazards. A promising strategy involves applying remote sensing image segmentation techniques to accurately identify IWs, thereby enabling predictions of their propagation velocity and direction. However, current IWs segmentation models struggle to balance computational efficiency and segmentation accuracy, often resulting in either excessive computational costs or inadequate performance. Motivated by recent developments in the Mamba2 architecture, this paper introduces the state-space model meets linear attention (SMLA), a novel segmentation framework specifically designed for IWs. The proposed hybrid architecture effectively integrates three key components: a feature-aware serialization (FAS) block to efficiently convert spatial features into sequences; a state-space model with linear attention (SSM-LA) block that synergizes a state-space model with linear attention for comprehensive feature extraction; and a decoder driven by hierarchical fusion and upsampling, which performs channel alignment and scale unification across multi-level features to ensure high-fidelity spatial detail recovery. Experiments conducted on a dataset of 484 synthetic-aperture radar (SAR) images containing IWs from the South China Sea achieved a mean Intersection over Union (MIoU) of 74.3%, surpassing competing methods evaluated on the same dataset. These results demonstrate the superior effectiveness of SMLA in extracting features of IWs from SAR imagery. [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: State-Space Model Meets Linear Attention: A Hybrid Architecture for Internal Wave Segmentation.
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  Data: <searchLink fieldCode="AR" term="%22An%2C+Zhijie%22">An, Zhijie</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Zhao%22">Li, Zhao</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Barintag%2C+Saheya%22">Barintag, Saheya</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhao%2C+Hongyu%22">Zhao, Hongyu</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yao%2C+Yanqing%22">Yao, Yanqing</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jiao%2C+Licheng%22">Jiao, Licheng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Gong%2C+Maoguo%22">Gong, Maoguo</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> gong@ieee.org</i>
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  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Sep2025, Vol. 17 Issue 17, p2969. 18p.
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  Data: <searchLink fieldCode="DE" term="%22Internal+waves%22">Internal waves</searchLink><br /><searchLink fieldCode="DE" term="%22Image+segmentation%22">Image segmentation</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing+by+radar%22">Remote sensing by radar</searchLink><br /><searchLink fieldCode="DE" term="%22State-space+methods%22">State-space methods</searchLink>
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  Label: Abstract
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  Data: Internal waves (IWs) play a crucial role in the transport of energy and matter within the ocean while also posing significant risks to marine engineering, navigation, and underwater communication systems. Consequently, effective segmentation methods are essential for mitigating their adverse impacts and minimizing associated hazards. A promising strategy involves applying remote sensing image segmentation techniques to accurately identify IWs, thereby enabling predictions of their propagation velocity and direction. However, current IWs segmentation models struggle to balance computational efficiency and segmentation accuracy, often resulting in either excessive computational costs or inadequate performance. Motivated by recent developments in the Mamba2 architecture, this paper introduces the state-space model meets linear attention (SMLA), a novel segmentation framework specifically designed for IWs. The proposed hybrid architecture effectively integrates three key components: a feature-aware serialization (FAS) block to efficiently convert spatial features into sequences; a state-space model with linear attention (SSM-LA) block that synergizes a state-space model with linear attention for comprehensive feature extraction; and a decoder driven by hierarchical fusion and upsampling, which performs channel alignment and scale unification across multi-level features to ensure high-fidelity spatial detail recovery. Experiments conducted on a dataset of 484 synthetic-aperture radar (SAR) images containing IWs from the South China Sea achieved a mean Intersection over Union (MIoU) of 74.3%, surpassing competing methods evaluated on the same dataset. These results demonstrate the superior effectiveness of SMLA in extracting features of IWs from SAR imagery. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  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/rs17172969
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        Text: English
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      – SubjectFull: Internal waves
        Type: general
      – SubjectFull: Image segmentation
        Type: general
      – SubjectFull: Feature extraction
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      – SubjectFull: Remote sensing
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      – SubjectFull: Remote sensing by radar
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      – SubjectFull: State-space methods
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      – TitleFull: State-Space Model Meets Linear Attention: A Hybrid Architecture for Internal Wave Segmentation.
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            NameFull: An, Zhijie
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              M: 09
              Text: Sep2025
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              Y: 2025
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