Minhong Sun , Han Yang , Zihan Xia , Fengjiao Gan , Zhao Huang , Zhiwen Zheng , Lou Zhao , Chunshan Liu , Zhaoyang Xu , Yun Lin , Guan Gui , Xiaoshuai Zhang , Xingru Huang , Jin Liu
{"title":"协调空频域协同驱动的地理空间特征合成增强SAR语义分割","authors":"Minhong Sun , Han Yang , Zihan Xia , Fengjiao Gan , Zhao Huang , Zhiwen Zheng , Lou Zhao , Chunshan Liu , Zhaoyang Xu , Yun Lin , Guan Gui , Xiaoshuai Zhang , Xingru Huang , Jin Liu","doi":"10.1016/j.jag.2025.104754","DOIUrl":null,"url":null,"abstract":"<div><div>The phase coherence of radar signals makes synthetic aperture radar (SAR) image analysis prone to significant challenges. Echo signal interference introduces speckle noise during imaging; noise appears as random fluctuations in pixel intensities. Besides, coherence exacerbates geometric distortions, complicating the accurate interpretation of spatial distributions within intricate geographic entities, thereby making it difficult to extract meaningful target information from SAR images. Addressing these challenges, this study introduces the DEcomposed-frequency PrOjection Network (Depo-Net), a segmentation-oriented model that mitigates SAR-specific interference through frequency-domain self-attention. It employs a dual-encoder structure for efficient semantic extraction and integrates Spatio-Frequency Synergistic Modulation (SFSM) to minimize speckle noise while maintaining structural integrity in the frequency domain. Additionally, the Harmonized Subspace Spectro-Temporal Attention (HSSTA) synthesizes Discrete Fourier and Wavelet Transform analyses to capture complex spatial correlations among geographic features. To mitigate noise amplification during decoding, the Pluri-frequency Mamba (purfMamba) module synergizes multi-dimensional spectral-spatial features, facilitating noise suppression during high-resolution restoration and maintaining a balance between global structure and local details. Results on three public SAR segmentation datasets demonstrate Depo-Net’s efficacy outperforming 22 previous State-of-the-Art (SOTA) methods while minimizing 95th Percentile Hausdorff Distance values. The complete code and model implementation is available on GitHub at <span><span>https://github.com/IMOP-lab/Depo-Net</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"143 ","pages":"Article 104754"},"PeriodicalIF":8.6000,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Harmonized spatial-frequency domain synergy driven geospatial feature synthesis for enhanced SAR semantic segmentation\",\"authors\":\"Minhong Sun , Han Yang , Zihan Xia , Fengjiao Gan , Zhao Huang , Zhiwen Zheng , Lou Zhao , Chunshan Liu , Zhaoyang Xu , Yun Lin , Guan Gui , Xiaoshuai Zhang , Xingru Huang , Jin Liu\",\"doi\":\"10.1016/j.jag.2025.104754\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The phase coherence of radar signals makes synthetic aperture radar (SAR) image analysis prone to significant challenges. Echo signal interference introduces speckle noise during imaging; noise appears as random fluctuations in pixel intensities. Besides, coherence exacerbates geometric distortions, complicating the accurate interpretation of spatial distributions within intricate geographic entities, thereby making it difficult to extract meaningful target information from SAR images. Addressing these challenges, this study introduces the DEcomposed-frequency PrOjection Network (Depo-Net), a segmentation-oriented model that mitigates SAR-specific interference through frequency-domain self-attention. It employs a dual-encoder structure for efficient semantic extraction and integrates Spatio-Frequency Synergistic Modulation (SFSM) to minimize speckle noise while maintaining structural integrity in the frequency domain. Additionally, the Harmonized Subspace Spectro-Temporal Attention (HSSTA) synthesizes Discrete Fourier and Wavelet Transform analyses to capture complex spatial correlations among geographic features. To mitigate noise amplification during decoding, the Pluri-frequency Mamba (purfMamba) module synergizes multi-dimensional spectral-spatial features, facilitating noise suppression during high-resolution restoration and maintaining a balance between global structure and local details. Results on three public SAR segmentation datasets demonstrate Depo-Net’s efficacy outperforming 22 previous State-of-the-Art (SOTA) methods while minimizing 95th Percentile Hausdorff Distance values. The complete code and model implementation is available on GitHub at <span><span>https://github.com/IMOP-lab/Depo-Net</span><svg><path></path></svg></span>.</div></div>\",\"PeriodicalId\":73423,\"journal\":{\"name\":\"International journal of applied earth observation and geoinformation : ITC journal\",\"volume\":\"143 \",\"pages\":\"Article 104754\"},\"PeriodicalIF\":8.6000,\"publicationDate\":\"2025-08-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International journal of applied earth observation and geoinformation : ITC journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1569843225004017\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"REMOTE SENSING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International journal of applied earth observation and geoinformation : ITC journal","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1569843225004017","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"REMOTE SENSING","Score":null,"Total":0}
Harmonized spatial-frequency domain synergy driven geospatial feature synthesis for enhanced SAR semantic segmentation
The phase coherence of radar signals makes synthetic aperture radar (SAR) image analysis prone to significant challenges. Echo signal interference introduces speckle noise during imaging; noise appears as random fluctuations in pixel intensities. Besides, coherence exacerbates geometric distortions, complicating the accurate interpretation of spatial distributions within intricate geographic entities, thereby making it difficult to extract meaningful target information from SAR images. Addressing these challenges, this study introduces the DEcomposed-frequency PrOjection Network (Depo-Net), a segmentation-oriented model that mitigates SAR-specific interference through frequency-domain self-attention. It employs a dual-encoder structure for efficient semantic extraction and integrates Spatio-Frequency Synergistic Modulation (SFSM) to minimize speckle noise while maintaining structural integrity in the frequency domain. Additionally, the Harmonized Subspace Spectro-Temporal Attention (HSSTA) synthesizes Discrete Fourier and Wavelet Transform analyses to capture complex spatial correlations among geographic features. To mitigate noise amplification during decoding, the Pluri-frequency Mamba (purfMamba) module synergizes multi-dimensional spectral-spatial features, facilitating noise suppression during high-resolution restoration and maintaining a balance between global structure and local details. Results on three public SAR segmentation datasets demonstrate Depo-Net’s efficacy outperforming 22 previous State-of-the-Art (SOTA) methods while minimizing 95th Percentile Hausdorff Distance values. The complete code and model implementation is available on GitHub at https://github.com/IMOP-lab/Depo-Net.
期刊介绍:
The International Journal of Applied Earth Observation and Geoinformation publishes original papers that utilize earth observation data for natural resource and environmental inventory and management. These data primarily originate from remote sensing platforms, including satellites and aircraft, supplemented by surface and subsurface measurements. Addressing natural resources such as forests, agricultural land, soils, and water, as well as environmental concerns like biodiversity, land degradation, and hazards, the journal explores conceptual and data-driven approaches. It covers geoinformation themes like capturing, databasing, visualization, interpretation, data quality, and spatial uncertainty.