Orthogonal-Heading Wavelength-Resolution SAR Image Stack Fusion-Based Foliage-Penetrating Vehicle Detection.
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| Title: | Orthogonal-Heading Wavelength-Resolution SAR Image Stack Fusion-Based Foliage-Penetrating Vehicle Detection. |
|---|---|
| Authors: | Zhang, Haonan1 (AUTHOR), An, Daoxiang1 (AUTHOR) daoxiangan@nudt.edu.cn |
| Source: | Remote Sensing. Mar2026, Vol. 18 Issue 5, p734. 20p. |
| Subjects: | Synthetic aperture radar, Matrix decomposition |
| Abstract: | Highlights: What are the main findings? The fusion of orthogonal-heading very-high-frequency (VHF) SAR imaging results could enrich the scattering characteristics of foliage-penetrating (FOPEN) targets, especially vehicle targets. RE is more suitable for measuring the sparse VHF SAR image stack than Shannon entropy. What are the implications of the main findings? Enhancing the detectability of the traditional image stack-based FOPEN motion target detection method by the orthogonal-heading VHF SAR fusion and RE metric. Enhancing the computational efficiency of the traditional image stack-based FOPEN motion target detection method by the low-rank sparse decomposition of VHF SAR image stack. This paper presents an orthogonal-heading wavelength-resolution SAR (WRSAR) target detection framework that fuses multi-heading image stacks for foliage-penetrating (FOPEN) vehicle detection. First, a low-rank–sparse decomposition is applied to very-high-frequency (VHF), ultra-wideband (UWB) WRSAR stacks to suppress vegetation clutter and enhance target contrast. The clutter-suppressed sparse stacks acquired from orthogonal headings are then fused to enrich target scattering characteristics. Finally, a Rayleigh-entropy statistic computed on the fused sparse stack is used to represent discontinuous positional changes. Based on the non-negative nature of WRSAR amplitudes for both clutter and FOPEN targets, we introduce a non-negative constrained tensor robust principal component analysis (NCTRPCA) to improve sparsity in the stack components. Furthermore, since Shannon differential entropy has no tunable parameter, we replace Shannon entropy with RE in this work and derive its closed-form expression for the proposed detector. Experiments on the publicly available multi-heading, multi-temporal CARABAS II dataset show that the proposed orthogonal-heading WRSAR fusion achieves higher FOPEN vehicle detection performance than recent state-of-the-art methods while maintaining moderate computational cost. [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.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 192639991 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Orthogonal-Heading Wavelength-Resolution SAR Image Stack Fusion-Based Foliage-Penetrating Vehicle Detection. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhang%2C+Haonan%22">Zhang, Haonan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22An%2C+Daoxiang%22">An, Daoxiang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> daoxiangan@nudt.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Mar2026, Vol. 18 Issue 5, p734. 20p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Synthetic+aperture+radar%22">Synthetic aperture radar</searchLink><br /><searchLink fieldCode="DE" term="%22Matrix+decomposition%22">Matrix decomposition</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Highlights: What are the main findings? The fusion of orthogonal-heading very-high-frequency (VHF) SAR imaging results could enrich the scattering characteristics of foliage-penetrating (FOPEN) targets, especially vehicle targets. RE is more suitable for measuring the sparse VHF SAR image stack than Shannon entropy. What are the implications of the main findings? Enhancing the detectability of the traditional image stack-based FOPEN motion target detection method by the orthogonal-heading VHF SAR fusion and RE metric. Enhancing the computational efficiency of the traditional image stack-based FOPEN motion target detection method by the low-rank sparse decomposition of VHF SAR image stack. This paper presents an orthogonal-heading wavelength-resolution SAR (WRSAR) target detection framework that fuses multi-heading image stacks for foliage-penetrating (FOPEN) vehicle detection. First, a low-rank–sparse decomposition is applied to very-high-frequency (VHF), ultra-wideband (UWB) WRSAR stacks to suppress vegetation clutter and enhance target contrast. The clutter-suppressed sparse stacks acquired from orthogonal headings are then fused to enrich target scattering characteristics. Finally, a Rayleigh-entropy statistic computed on the fused sparse stack is used to represent discontinuous positional changes. Based on the non-negative nature of WRSAR amplitudes for both clutter and FOPEN targets, we introduce a non-negative constrained tensor robust principal component analysis (NCTRPCA) to improve sparsity in the stack components. Furthermore, since Shannon differential entropy has no tunable parameter, we replace Shannon entropy with RE in this work and derive its closed-form expression for the proposed detector. Experiments on the publicly available multi-heading, multi-temporal CARABAS II dataset show that the proposed orthogonal-heading WRSAR fusion achieves higher FOPEN vehicle detection performance than recent state-of-the-art methods while maintaining moderate computational cost. [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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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/rs18050734 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 20 StartPage: 734 Subjects: – SubjectFull: Synthetic aperture radar Type: general – SubjectFull: Matrix decomposition Type: general Titles: – TitleFull: Orthogonal-Heading Wavelength-Resolution SAR Image Stack Fusion-Based Foliage-Penetrating Vehicle Detection. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhang, Haonan – PersonEntity: Name: NameFull: An, Daoxiang IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 18 – Type: issue Value: 5 Titles: – TitleFull: Remote Sensing Type: main |
| ResultId | 1 |
