Ship detection based on deep learning using SAR imagery: a systematic literature review
Abstract This study adheres to a set of guidelines for performing an SLR. The mission of the SLR is to find publications, publishers, deep learning types, improved and amended deep learning techniques, impacts, proactive approaches, key parameters, and applications in ship detection by SAR images, a...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Yasir, Muhammad [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Soft Computing - Springer-Verlag, 2003, 27(2022), 1 vom: 06. Okt., Seite 63-84 |
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| Übergeordnetes Werk: |
volume:27 ; year:2022 ; number:1 ; day:06 ; month:10 ; pages:63-84 |
| Links: |
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| DOI / URN: |
10.1007/s00500-022-07522-w |
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SPR048956163 |
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| 520 | |a Abstract This study adheres to a set of guidelines for performing an SLR. The mission of the SLR is to find publications, publishers, deep learning types, improved and amended deep learning techniques, impacts, proactive approaches, key parameters, and applications in ship detection by SAR images, as well as extract current research directions, limitations, and unsolved challenges to give understanding and suggestions for future research. To minimize any complications, the information was gathered from significant research publications published in decent journals between 2016 and 2022. The proceedings of conferences and seminars, as well as other online resources, are not included. A total of 81 primary studies were selected based on pre-determined exclusion, inclusion, and quality characteristics. The literature review addressed several important issues, including key methods considered by researchers in the field of ship identification by SAR images, various types of DL limitations and their alternating solutions proposed for ship detection by SAR imagery analysis, and several types of proactive approaches suggested in the literature to mitigate risks associated with ship detection by SAR images, and types of SAR imageries significances reported in the ship detection analysis. Despite substantial research and development of various deep learning algorithms, the findings demonstrate that there is still a scarcity of organized knowledge that allows deep learning to be applied for essential applications in the ship detection by SAR imagery field. Furthermore, DL techniques to recognize ships in SAR images have not been fully exploited, necessitating future research. The findings point to the necessity for more research into deep learning approaches, as well as the development of an authentic process for correct results extracted from SAR data for ship detection. Researchers will be able to view current studies on deep learning techniques with the help of the recommended research, which can then be used as evidence for future research. | ||
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10.1007/s00500-022-07522-w doi (DE-627)SPR048956163 (SPR)s00500-022-07522-w-e DE-627 ger DE-627 rakwb eng Yasir, Muhammad verfasserin aut Ship detection based on deep learning using SAR imagery: a systematic literature review 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract This study adheres to a set of guidelines for performing an SLR. The mission of the SLR is to find publications, publishers, deep learning types, improved and amended deep learning techniques, impacts, proactive approaches, key parameters, and applications in ship detection by SAR images, as well as extract current research directions, limitations, and unsolved challenges to give understanding and suggestions for future research. To minimize any complications, the information was gathered from significant research publications published in decent journals between 2016 and 2022. The proceedings of conferences and seminars, as well as other online resources, are not included. A total of 81 primary studies were selected based on pre-determined exclusion, inclusion, and quality characteristics. The literature review addressed several important issues, including key methods considered by researchers in the field of ship identification by SAR images, various types of DL limitations and their alternating solutions proposed for ship detection by SAR imagery analysis, and several types of proactive approaches suggested in the literature to mitigate risks associated with ship detection by SAR images, and types of SAR imageries significances reported in the ship detection analysis. Despite substantial research and development of various deep learning algorithms, the findings demonstrate that there is still a scarcity of organized knowledge that allows deep learning to be applied for essential applications in the ship detection by SAR imagery field. Furthermore, DL techniques to recognize ships in SAR images have not been fully exploited, necessitating future research. The findings point to the necessity for more research into deep learning approaches, as well as the development of an authentic process for correct results extracted from SAR data for ship detection. Researchers will be able to view current studies on deep learning techniques with the help of the recommended research, which can then be used as evidence for future research. Synthetic aperture radar (SAR) (dpeaa)DE-He213 Ship detection (dpeaa)DE-He213 Deep learning (DL) (dpeaa)DE-He213 Systematic literature review (SLR) (dpeaa)DE-He213 Jianhua, Wan aut Mingming, Xu aut Hui, Sheng aut Zhe, Zeng aut Shanwei, Liu aut Colak, Arife Tugsan Isiacik aut Hossain, Md Sakaouth aut Enthalten in Soft Computing Springer-Verlag, 2003 27(2022), 1 vom: 06. Okt., Seite 63-84 (DE-627)SPR006469531 nnns volume:27 year:2022 number:1 day:06 month:10 pages:63-84 https://dx.doi.org/10.1007/s00500-022-07522-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 27 2022 1 06 10 63-84 |
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10.1007/s00500-022-07522-w doi (DE-627)SPR048956163 (SPR)s00500-022-07522-w-e DE-627 ger DE-627 rakwb eng Yasir, Muhammad verfasserin aut Ship detection based on deep learning using SAR imagery: a systematic literature review 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract This study adheres to a set of guidelines for performing an SLR. The mission of the SLR is to find publications, publishers, deep learning types, improved and amended deep learning techniques, impacts, proactive approaches, key parameters, and applications in ship detection by SAR images, as well as extract current research directions, limitations, and unsolved challenges to give understanding and suggestions for future research. To minimize any complications, the information was gathered from significant research publications published in decent journals between 2016 and 2022. The proceedings of conferences and seminars, as well as other online resources, are not included. A total of 81 primary studies were selected based on pre-determined exclusion, inclusion, and quality characteristics. The literature review addressed several important issues, including key methods considered by researchers in the field of ship identification by SAR images, various types of DL limitations and their alternating solutions proposed for ship detection by SAR imagery analysis, and several types of proactive approaches suggested in the literature to mitigate risks associated with ship detection by SAR images, and types of SAR imageries significances reported in the ship detection analysis. Despite substantial research and development of various deep learning algorithms, the findings demonstrate that there is still a scarcity of organized knowledge that allows deep learning to be applied for essential applications in the ship detection by SAR imagery field. Furthermore, DL techniques to recognize ships in SAR images have not been fully exploited, necessitating future research. The findings point to the necessity for more research into deep learning approaches, as well as the development of an authentic process for correct results extracted from SAR data for ship detection. Researchers will be able to view current studies on deep learning techniques with the help of the recommended research, which can then be used as evidence for future research. Synthetic aperture radar (SAR) (dpeaa)DE-He213 Ship detection (dpeaa)DE-He213 Deep learning (DL) (dpeaa)DE-He213 Systematic literature review (SLR) (dpeaa)DE-He213 Jianhua, Wan aut Mingming, Xu aut Hui, Sheng aut Zhe, Zeng aut Shanwei, Liu aut Colak, Arife Tugsan Isiacik aut Hossain, Md Sakaouth aut Enthalten in Soft Computing Springer-Verlag, 2003 27(2022), 1 vom: 06. Okt., Seite 63-84 (DE-627)SPR006469531 nnns volume:27 year:2022 number:1 day:06 month:10 pages:63-84 https://dx.doi.org/10.1007/s00500-022-07522-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 27 2022 1 06 10 63-84 |
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10.1007/s00500-022-07522-w doi (DE-627)SPR048956163 (SPR)s00500-022-07522-w-e DE-627 ger DE-627 rakwb eng Yasir, Muhammad verfasserin aut Ship detection based on deep learning using SAR imagery: a systematic literature review 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract This study adheres to a set of guidelines for performing an SLR. The mission of the SLR is to find publications, publishers, deep learning types, improved and amended deep learning techniques, impacts, proactive approaches, key parameters, and applications in ship detection by SAR images, as well as extract current research directions, limitations, and unsolved challenges to give understanding and suggestions for future research. To minimize any complications, the information was gathered from significant research publications published in decent journals between 2016 and 2022. The proceedings of conferences and seminars, as well as other online resources, are not included. A total of 81 primary studies were selected based on pre-determined exclusion, inclusion, and quality characteristics. The literature review addressed several important issues, including key methods considered by researchers in the field of ship identification by SAR images, various types of DL limitations and their alternating solutions proposed for ship detection by SAR imagery analysis, and several types of proactive approaches suggested in the literature to mitigate risks associated with ship detection by SAR images, and types of SAR imageries significances reported in the ship detection analysis. Despite substantial research and development of various deep learning algorithms, the findings demonstrate that there is still a scarcity of organized knowledge that allows deep learning to be applied for essential applications in the ship detection by SAR imagery field. Furthermore, DL techniques to recognize ships in SAR images have not been fully exploited, necessitating future research. The findings point to the necessity for more research into deep learning approaches, as well as the development of an authentic process for correct results extracted from SAR data for ship detection. Researchers will be able to view current studies on deep learning techniques with the help of the recommended research, which can then be used as evidence for future research. Synthetic aperture radar (SAR) (dpeaa)DE-He213 Ship detection (dpeaa)DE-He213 Deep learning (DL) (dpeaa)DE-He213 Systematic literature review (SLR) (dpeaa)DE-He213 Jianhua, Wan aut Mingming, Xu aut Hui, Sheng aut Zhe, Zeng aut Shanwei, Liu aut Colak, Arife Tugsan Isiacik aut Hossain, Md Sakaouth aut Enthalten in Soft Computing Springer-Verlag, 2003 27(2022), 1 vom: 06. Okt., Seite 63-84 (DE-627)SPR006469531 nnns volume:27 year:2022 number:1 day:06 month:10 pages:63-84 https://dx.doi.org/10.1007/s00500-022-07522-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 27 2022 1 06 10 63-84 |
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10.1007/s00500-022-07522-w doi (DE-627)SPR048956163 (SPR)s00500-022-07522-w-e DE-627 ger DE-627 rakwb eng Yasir, Muhammad verfasserin aut Ship detection based on deep learning using SAR imagery: a systematic literature review 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract This study adheres to a set of guidelines for performing an SLR. The mission of the SLR is to find publications, publishers, deep learning types, improved and amended deep learning techniques, impacts, proactive approaches, key parameters, and applications in ship detection by SAR images, as well as extract current research directions, limitations, and unsolved challenges to give understanding and suggestions for future research. To minimize any complications, the information was gathered from significant research publications published in decent journals between 2016 and 2022. The proceedings of conferences and seminars, as well as other online resources, are not included. A total of 81 primary studies were selected based on pre-determined exclusion, inclusion, and quality characteristics. The literature review addressed several important issues, including key methods considered by researchers in the field of ship identification by SAR images, various types of DL limitations and their alternating solutions proposed for ship detection by SAR imagery analysis, and several types of proactive approaches suggested in the literature to mitigate risks associated with ship detection by SAR images, and types of SAR imageries significances reported in the ship detection analysis. Despite substantial research and development of various deep learning algorithms, the findings demonstrate that there is still a scarcity of organized knowledge that allows deep learning to be applied for essential applications in the ship detection by SAR imagery field. Furthermore, DL techniques to recognize ships in SAR images have not been fully exploited, necessitating future research. The findings point to the necessity for more research into deep learning approaches, as well as the development of an authentic process for correct results extracted from SAR data for ship detection. Researchers will be able to view current studies on deep learning techniques with the help of the recommended research, which can then be used as evidence for future research. Synthetic aperture radar (SAR) (dpeaa)DE-He213 Ship detection (dpeaa)DE-He213 Deep learning (DL) (dpeaa)DE-He213 Systematic literature review (SLR) (dpeaa)DE-He213 Jianhua, Wan aut Mingming, Xu aut Hui, Sheng aut Zhe, Zeng aut Shanwei, Liu aut Colak, Arife Tugsan Isiacik aut Hossain, Md Sakaouth aut Enthalten in Soft Computing Springer-Verlag, 2003 27(2022), 1 vom: 06. Okt., Seite 63-84 (DE-627)SPR006469531 nnns volume:27 year:2022 number:1 day:06 month:10 pages:63-84 https://dx.doi.org/10.1007/s00500-022-07522-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 27 2022 1 06 10 63-84 |
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10.1007/s00500-022-07522-w doi (DE-627)SPR048956163 (SPR)s00500-022-07522-w-e DE-627 ger DE-627 rakwb eng Yasir, Muhammad verfasserin aut Ship detection based on deep learning using SAR imagery: a systematic literature review 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract This study adheres to a set of guidelines for performing an SLR. The mission of the SLR is to find publications, publishers, deep learning types, improved and amended deep learning techniques, impacts, proactive approaches, key parameters, and applications in ship detection by SAR images, as well as extract current research directions, limitations, and unsolved challenges to give understanding and suggestions for future research. To minimize any complications, the information was gathered from significant research publications published in decent journals between 2016 and 2022. The proceedings of conferences and seminars, as well as other online resources, are not included. A total of 81 primary studies were selected based on pre-determined exclusion, inclusion, and quality characteristics. The literature review addressed several important issues, including key methods considered by researchers in the field of ship identification by SAR images, various types of DL limitations and their alternating solutions proposed for ship detection by SAR imagery analysis, and several types of proactive approaches suggested in the literature to mitigate risks associated with ship detection by SAR images, and types of SAR imageries significances reported in the ship detection analysis. Despite substantial research and development of various deep learning algorithms, the findings demonstrate that there is still a scarcity of organized knowledge that allows deep learning to be applied for essential applications in the ship detection by SAR imagery field. Furthermore, DL techniques to recognize ships in SAR images have not been fully exploited, necessitating future research. The findings point to the necessity for more research into deep learning approaches, as well as the development of an authentic process for correct results extracted from SAR data for ship detection. Researchers will be able to view current studies on deep learning techniques with the help of the recommended research, which can then be used as evidence for future research. Synthetic aperture radar (SAR) (dpeaa)DE-He213 Ship detection (dpeaa)DE-He213 Deep learning (DL) (dpeaa)DE-He213 Systematic literature review (SLR) (dpeaa)DE-He213 Jianhua, Wan aut Mingming, Xu aut Hui, Sheng aut Zhe, Zeng aut Shanwei, Liu aut Colak, Arife Tugsan Isiacik aut Hossain, Md Sakaouth aut Enthalten in Soft Computing Springer-Verlag, 2003 27(2022), 1 vom: 06. Okt., Seite 63-84 (DE-627)SPR006469531 nnns volume:27 year:2022 number:1 day:06 month:10 pages:63-84 https://dx.doi.org/10.1007/s00500-022-07522-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 27 2022 1 06 10 63-84 |
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ship detection based on deep learning using sar imagery: a systematic literature review |
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Ship detection based on deep learning using SAR imagery: a systematic literature review |
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Abstract This study adheres to a set of guidelines for performing an SLR. The mission of the SLR is to find publications, publishers, deep learning types, improved and amended deep learning techniques, impacts, proactive approaches, key parameters, and applications in ship detection by SAR images, as well as extract current research directions, limitations, and unsolved challenges to give understanding and suggestions for future research. To minimize any complications, the information was gathered from significant research publications published in decent journals between 2016 and 2022. The proceedings of conferences and seminars, as well as other online resources, are not included. A total of 81 primary studies were selected based on pre-determined exclusion, inclusion, and quality characteristics. The literature review addressed several important issues, including key methods considered by researchers in the field of ship identification by SAR images, various types of DL limitations and their alternating solutions proposed for ship detection by SAR imagery analysis, and several types of proactive approaches suggested in the literature to mitigate risks associated with ship detection by SAR images, and types of SAR imageries significances reported in the ship detection analysis. Despite substantial research and development of various deep learning algorithms, the findings demonstrate that there is still a scarcity of organized knowledge that allows deep learning to be applied for essential applications in the ship detection by SAR imagery field. Furthermore, DL techniques to recognize ships in SAR images have not been fully exploited, necessitating future research. The findings point to the necessity for more research into deep learning approaches, as well as the development of an authentic process for correct results extracted from SAR data for ship detection. Researchers will be able to view current studies on deep learning techniques with the help of the recommended research, which can then be used as evidence for future research. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Abstract This study adheres to a set of guidelines for performing an SLR. The mission of the SLR is to find publications, publishers, deep learning types, improved and amended deep learning techniques, impacts, proactive approaches, key parameters, and applications in ship detection by SAR images, as well as extract current research directions, limitations, and unsolved challenges to give understanding and suggestions for future research. To minimize any complications, the information was gathered from significant research publications published in decent journals between 2016 and 2022. The proceedings of conferences and seminars, as well as other online resources, are not included. A total of 81 primary studies were selected based on pre-determined exclusion, inclusion, and quality characteristics. The literature review addressed several important issues, including key methods considered by researchers in the field of ship identification by SAR images, various types of DL limitations and their alternating solutions proposed for ship detection by SAR imagery analysis, and several types of proactive approaches suggested in the literature to mitigate risks associated with ship detection by SAR images, and types of SAR imageries significances reported in the ship detection analysis. Despite substantial research and development of various deep learning algorithms, the findings demonstrate that there is still a scarcity of organized knowledge that allows deep learning to be applied for essential applications in the ship detection by SAR imagery field. Furthermore, DL techniques to recognize ships in SAR images have not been fully exploited, necessitating future research. The findings point to the necessity for more research into deep learning approaches, as well as the development of an authentic process for correct results extracted from SAR data for ship detection. Researchers will be able to view current studies on deep learning techniques with the help of the recommended research, which can then be used as evidence for future research. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Abstract This study adheres to a set of guidelines for performing an SLR. The mission of the SLR is to find publications, publishers, deep learning types, improved and amended deep learning techniques, impacts, proactive approaches, key parameters, and applications in ship detection by SAR images, as well as extract current research directions, limitations, and unsolved challenges to give understanding and suggestions for future research. To minimize any complications, the information was gathered from significant research publications published in decent journals between 2016 and 2022. The proceedings of conferences and seminars, as well as other online resources, are not included. A total of 81 primary studies were selected based on pre-determined exclusion, inclusion, and quality characteristics. The literature review addressed several important issues, including key methods considered by researchers in the field of ship identification by SAR images, various types of DL limitations and their alternating solutions proposed for ship detection by SAR imagery analysis, and several types of proactive approaches suggested in the literature to mitigate risks associated with ship detection by SAR images, and types of SAR imageries significances reported in the ship detection analysis. Despite substantial research and development of various deep learning algorithms, the findings demonstrate that there is still a scarcity of organized knowledge that allows deep learning to be applied for essential applications in the ship detection by SAR imagery field. Furthermore, DL techniques to recognize ships in SAR images have not been fully exploited, necessitating future research. The findings point to the necessity for more research into deep learning approaches, as well as the development of an authentic process for correct results extracted from SAR data for ship detection. Researchers will be able to view current studies on deep learning techniques with the help of the recommended research, which can then be used as evidence for future research. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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