Sparse anchoring guided high-resolution capsule network for geospatial object detection from remote sensing imagery
[Display omitted] •High-resolution capsule network for semantically strong and spatially accurate feature extraction.•Capsule-based efficient self-attention module for feature quality and robustness promotion.•Sparse anchoring network for lightweight, high-quality region proposal generation.•Oriente...
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| Veröffentlicht in: | International journal of applied earth observation and geoinformation Jg. 104; S. 102548 |
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| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Elsevier B.V
15.12.2021
Elsevier |
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| ISSN: | 1569-8432, 1872-826X |
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| Abstract | [Display omitted]
•High-resolution capsule network for semantically strong and spatially accurate feature extraction.•Capsule-based efficient self-attention module for feature quality and robustness promotion.•Sparse anchoring network for lightweight, high-quality region proposal generation.•Oriented region proposal generation for arbitrarily-oriented geospatial object detection.
As the optical remote sensing techniques keep developing with a rapid pace, remote sensing images are positively considered in many fields. Accordingly, a great number of algorithms have been exploited for remote sensing image interpretation purposes. Thereinto, object recognition acts as an important ingredient to many applications. However, to achieve highly accurate object recognition is still challengeable caused by the orientation and size diversities, spatial distribution and density variations, shape and aspect ratio irregularities, occlusion and shadow impacts, as well as complex texture and surrounding environment changes. In this paper, a sparse anchoring guided high-resolution capsule network (SAHR-CapsNet) is proposed for geospatial object detection based on remote sensing images. First, formulated with the multibranch high-resolution capsule network architecture assisted by multiscale feature propagation and fusion, the SAHR-CapsNet can extract semantically strong and spatially accurate feature semantics at multiple scales. Second, integrated with the efficient capsule-based self-attention module, the SAHR-CapsNet functions promisingly to attend to target-specific spatial features and informative channel features. Finally, adopted with the capsule-based sparse anchoring network, the SAHR-CapsNet performs efficiently in generating a fixed number of lightweight, high-quality sparse region proposals. Quantitative assessments and comparative analyses on two challenging remote sensing image datasets demonstrate the applicability and effectiveness of the developed SAHR-CapsNet for geospatial object detection applications. |
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| AbstractList | As the optical remote sensing techniques keep developing with a rapid pace, remote sensing images are positively considered in many fields. Accordingly, a great number of algorithms have been exploited for remote sensing image interpretation purposes. Thereinto, object recognition acts as an important ingredient to many applications. However, to achieve highly accurate object recognition is still challengeable caused by the orientation and size diversities, spatial distribution and density variations, shape and aspect ratio irregularities, occlusion and shadow impacts, as well as complex texture and surrounding environment changes. In this paper, a sparse anchoring guided high-resolution capsule network (SAHR-CapsNet) is proposed for geospatial object detection based on remote sensing images. First, formulated with the multibranch high-resolution capsule network architecture assisted by multiscale feature propagation and fusion, the SAHR-CapsNet can extract semantically strong and spatially accurate feature semantics at multiple scales. Second, integrated with the efficient capsule-based self-attention module, the SAHR-CapsNet functions promisingly to attend to target-specific spatial features and informative channel features. Finally, adopted with the capsule-based sparse anchoring network, the SAHR-CapsNet performs efficiently in generating a fixed number of lightweight, high-quality sparse region proposals. Quantitative assessments and comparative analyses on two challenging remote sensing image datasets demonstrate the applicability and effectiveness of the developed SAHR-CapsNet for geospatial object detection applications. [Display omitted] •High-resolution capsule network for semantically strong and spatially accurate feature extraction.•Capsule-based efficient self-attention module for feature quality and robustness promotion.•Sparse anchoring network for lightweight, high-quality region proposal generation.•Oriented region proposal generation for arbitrarily-oriented geospatial object detection. As the optical remote sensing techniques keep developing with a rapid pace, remote sensing images are positively considered in many fields. Accordingly, a great number of algorithms have been exploited for remote sensing image interpretation purposes. Thereinto, object recognition acts as an important ingredient to many applications. However, to achieve highly accurate object recognition is still challengeable caused by the orientation and size diversities, spatial distribution and density variations, shape and aspect ratio irregularities, occlusion and shadow impacts, as well as complex texture and surrounding environment changes. In this paper, a sparse anchoring guided high-resolution capsule network (SAHR-CapsNet) is proposed for geospatial object detection based on remote sensing images. First, formulated with the multibranch high-resolution capsule network architecture assisted by multiscale feature propagation and fusion, the SAHR-CapsNet can extract semantically strong and spatially accurate feature semantics at multiple scales. Second, integrated with the efficient capsule-based self-attention module, the SAHR-CapsNet functions promisingly to attend to target-specific spatial features and informative channel features. Finally, adopted with the capsule-based sparse anchoring network, the SAHR-CapsNet performs efficiently in generating a fixed number of lightweight, high-quality sparse region proposals. Quantitative assessments and comparative analyses on two challenging remote sensing image datasets demonstrate the applicability and effectiveness of the developed SAHR-CapsNet for geospatial object detection applications. |
| ArticleNumber | 102548 |
| Author | Tang, E Zhang, Yongjun Jiang, Mingxin Yu, Changhui Wang, Jun Qiang, Hao Yu, Yongtao Li, Jonathan |
| Author_xml | – sequence: 1 givenname: Yongtao orcidid: 0000-0001-7204-9346 surname: Yu fullname: Yu, Yongtao email: allennessy@hyit.edu.cn organization: Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, JS 223003, China – sequence: 2 givenname: Jun surname: Wang fullname: Wang, Jun organization: Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, JS 223003, China – sequence: 3 givenname: Hao surname: Qiang fullname: Qiang, Hao organization: Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, JS 223003, China – sequence: 4 givenname: Mingxin surname: Jiang fullname: Jiang, Mingxin organization: Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, JS 223003, China – sequence: 5 givenname: E surname: Tang fullname: Tang, E organization: Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, JS 223003, China – sequence: 6 givenname: Changhui surname: Yu fullname: Yu, Changhui organization: Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, JS 223003, China – sequence: 7 givenname: Yongjun surname: Zhang fullname: Zhang, Yongjun organization: Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, JS 223003, China – sequence: 8 givenname: Jonathan surname: Li fullname: Li, Jonathan organization: Department of Geography and Environmental Management, University of Waterloo, Waterloo, ON N2L3G1, Canada |
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| Keywords | Region proposal Object recognition Capsule network Capsule attention Sparse anchoring Remote sensing imagery |
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•High-resolution capsule network for semantically strong and spatially accurate feature extraction.•Capsule-based efficient self-attention... As the optical remote sensing techniques keep developing with a rapid pace, remote sensing images are positively considered in many fields. Accordingly, a... |
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| Title | Sparse anchoring guided high-resolution capsule network for geospatial object detection from remote sensing imagery |
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