Deep-Learning-Based Semantic Segmentation for Remote Sensing: A Bibliometric Literature Review
Deep learning (DL) has emerged as a powerful technique for a wide range of computer vision applications. Consequently, DL is also being adopted to process geospatial and remote sensing (RS) images. As these methods are sporadic over different studies, many review papers have also been published to g...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10301677/ |
| _version_ | 1797376402941542400 |
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| author | Kazi Rakib Hasan Anamika Biswas Tuli Md. Al-Masrur Khan Seong-Hoon Kee Md Abdus Samad Abdullah-Al Nahid |
| author_facet | Kazi Rakib Hasan Anamika Biswas Tuli Md. Al-Masrur Khan Seong-Hoon Kee Md Abdus Samad Abdullah-Al Nahid |
| author_sort | Kazi Rakib Hasan |
| collection | DOAJ |
| description | Deep learning (DL) has emerged as a powerful technique for a wide range of computer vision applications. Consequently, DL is also being adopted to process geospatial and remote sensing (RS) images. As these methods are sporadic over different studies, many review papers have also been published to gather the approaches and summarize the existing models in this field. However, a state-of-the-art review paper is still scarce in this field that will present a bibliometric analysis as well as a critical analysis of the recent works. Therefore, this article aims to spur the researchers with a bibliometric analysis to identify the current research trend. As a research sample, in total, 281 related papers were collected from the Web of Science source, and bibliometric analysis was accomplished using VOSviewer software. Among the collection of associated works from the database, 28 papers were selected according to the defined criteria for detailed analysis. Besides this, a few research questions were generated to extract necessary information from the literature for extracting the pros and cons of the selected works. DL techniques were applied in these works and achieved results. Furthermore, the papers were also categorized based on the addressed RS application domain. |
| first_indexed | 2024-03-08T19:38:07Z |
| format | Article |
| id | doaj.art-37ad33393fa544899424be15e9824df3 |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-03-08T19:38:07Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-37ad33393fa544899424be15e9824df32023-12-26T00:01:08ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352024-01-01171390141810.1109/JSTARS.2023.332831510301677Deep-Learning-Based Semantic Segmentation for Remote Sensing: A Bibliometric Literature ReviewKazi Rakib Hasan0https://orcid.org/0000-0002-9967-1538Anamika Biswas Tuli1https://orcid.org/0009-0007-9155-9051Md. Al-Masrur Khan2https://orcid.org/0000-0002-1729-4071Seong-Hoon Kee3https://orcid.org/0000-0002-7743-4881Md Abdus Samad4https://orcid.org/0000-0002-1990-6924Abdullah-Al Nahid5https://orcid.org/0000-0003-2391-5767Electronics and Communication Engineering Discipline, Khulna University, Khulna, BangladeshElectronics and Communication Engineering Discipline, Khulna University, Khulna, BangladeshDepartment of ICT Integrated Ocean Smart Cities Engineering, Dong-A University, Busan, South KoreaDepartment of ICT Integrated Ocean Smart Cities Engineering, Dong-A University, Busan, South KoreaDepartment of Information and Communication Engineering, Yeungnam University, Gyeongsan-si, South KoreaElectronics and Communication Engineering Discipline, Khulna University, Khulna, BangladeshDeep learning (DL) has emerged as a powerful technique for a wide range of computer vision applications. Consequently, DL is also being adopted to process geospatial and remote sensing (RS) images. As these methods are sporadic over different studies, many review papers have also been published to gather the approaches and summarize the existing models in this field. However, a state-of-the-art review paper is still scarce in this field that will present a bibliometric analysis as well as a critical analysis of the recent works. Therefore, this article aims to spur the researchers with a bibliometric analysis to identify the current research trend. As a research sample, in total, 281 related papers were collected from the Web of Science source, and bibliometric analysis was accomplished using VOSviewer software. Among the collection of associated works from the database, 28 papers were selected according to the defined criteria for detailed analysis. Besides this, a few research questions were generated to extract necessary information from the literature for extracting the pros and cons of the selected works. DL techniques were applied in these works and achieved results. Furthermore, the papers were also categorized based on the addressed RS application domain.https://ieeexplore.ieee.org/document/10301677/Bibliometric analysisdeep learningremote sensing (RS)segmentationVOSviewer |
| spellingShingle | Kazi Rakib Hasan Anamika Biswas Tuli Md. Al-Masrur Khan Seong-Hoon Kee Md Abdus Samad Abdullah-Al Nahid Deep-Learning-Based Semantic Segmentation for Remote Sensing: A Bibliometric Literature Review IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Bibliometric analysis deep learning remote sensing (RS) segmentation VOSviewer |
| title | Deep-Learning-Based Semantic Segmentation for Remote Sensing: A Bibliometric Literature Review |
| title_full | Deep-Learning-Based Semantic Segmentation for Remote Sensing: A Bibliometric Literature Review |
| title_fullStr | Deep-Learning-Based Semantic Segmentation for Remote Sensing: A Bibliometric Literature Review |
| title_full_unstemmed | Deep-Learning-Based Semantic Segmentation for Remote Sensing: A Bibliometric Literature Review |
| title_short | Deep-Learning-Based Semantic Segmentation for Remote Sensing: A Bibliometric Literature Review |
| title_sort | deep learning based semantic segmentation for remote sensing a bibliometric literature review |
| topic | Bibliometric analysis deep learning remote sensing (RS) segmentation VOSviewer |
| url | https://ieeexplore.ieee.org/document/10301677/ |
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