A Deep Learning Approach for Ship Detection Using Satellite Imagery Article Swipe
Alakh Niranjan
,
Sparsh Patial
,
Aditya Aryan
,
Akshat Mittal
,
Tanupriya Choudhury
,
Hamidreza Rabiei‐Dastjerdi
,
Praveen Kumar
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.4108/eetiot.5435
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.4108/eetiot.5435
INTRODUCTION: This paper addresses ship detection in satellite imagery through a deep learning approach, vital for maritime applications. Traditional methods face challenges with large datasets, motivating the adoption of deep learning techniques. OBJECTIVES: The primary objective is to present an algorithmic methodology for U-Net model training, focusing on achieving accuracy, efficiency, and robust ship detection. Overcoming manual limitations and enhancing real-time monitoring capabilities are key objectives. METHOD: The methodology involves dataset collection from Copernicus Open Hub, employing run-length encoding for efficient preprocessing, and utilizing a U-Net model trained on Sentinel-2 images. Data manipulation includes run-length encoding, masking, and balanced dataset preprocessing. RESULT: Results demonstrate the proposed deep learning model's effectiveness in handling diverse datasets, ensuring accuracy through U-Net architecture, and addressing imbalances. The algorithmic process showcases proficiency in ship detection. CONCLUSION: In conclusion, this paper contributes a comprehensive methodology for ship detection, significantly advancing accuracy, efficiency, and robustness in maritime applications. The U-Net-based model successfully automates ship detection, promising real-time monitoring enhancements and improved maritime security.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.4108/eetiot.5435
- https://publications.eai.eu/index.php/IoT/article/download/5435/3009
- OA Status
- diamond
- Cited By
- 10
- References
- 16
- Related Works
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- OpenAlex ID
- https://openalex.org/W4392848012
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Raw OpenAlex JSON
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https://openalex.org/W4392848012Canonical identifier for this work in OpenAlex
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https://doi.org/10.4108/eetiot.5435Digital Object Identifier
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A Deep Learning Approach for Ship Detection Using Satellite ImageryWork title
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articleOpenAlex work type
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enPrimary language
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2024Year of publication
- Publication date
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2024-03-15Full publication date if available
- Authors
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Alakh Niranjan, Sparsh Patial, Aditya Aryan, Akshat Mittal, Tanupriya Choudhury, Hamidreza Rabiei‐Dastjerdi, Praveen KumarList of authors in order
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https://doi.org/10.4108/eetiot.5435Publisher landing page
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https://publications.eai.eu/index.php/IoT/article/download/5435/3009Direct link to full text PDF
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://publications.eai.eu/index.php/IoT/article/download/5435/3009Direct OA link when available
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Deep learning, Satellite imagery, Satellite, Artificial intelligence, Remote sensing, Computer science, Geology, Engineering, Aerospace engineeringTop concepts (fields/topics) attached by OpenAlex
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10Total citation count in OpenAlex
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2025: 10Per-year citation counts (last 5 years)
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16Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.for | 15, 42, 79, 139 |
| abstract_inverted_index.key | 64 |
| abstract_inverted_index.the | 26, 104 |
| abstract_inverted_index.Data | 91 |
| abstract_inverted_index.Hub, | 75 |
| abstract_inverted_index.Open | 74 |
| abstract_inverted_index.This | 1 |
| abstract_inverted_index.deep | 11, 29, 106 |
| abstract_inverted_index.face | 20 |
| abstract_inverted_index.from | 72 |
| abstract_inverted_index.ship | 4, 53, 128, 140, 156 |
| abstract_inverted_index.this | 133 |
| abstract_inverted_index.with | 22 |
| abstract_inverted_index.U-Net | 43, 85, 117 |
| abstract_inverted_index.large | 23 |
| abstract_inverted_index.model | 44, 86, 153 |
| abstract_inverted_index.paper | 2, 134 |
| abstract_inverted_index.vital | 14 |
| abstract_inverted_index.manual | 56 |
| abstract_inverted_index.robust | 52 |
| abstract_inverted_index.METHOD: | 66 |
| abstract_inverted_index.RESULT: | 101 |
| abstract_inverted_index.Results | 102 |
| abstract_inverted_index.dataset | 70, 99 |
| abstract_inverted_index.diverse | 112 |
| abstract_inverted_index.imagery | 8 |
| abstract_inverted_index.images. | 90 |
| abstract_inverted_index.methods | 19 |
| abstract_inverted_index.model's | 108 |
| abstract_inverted_index.present | 38 |
| abstract_inverted_index.primary | 34 |
| abstract_inverted_index.process | 124 |
| abstract_inverted_index.through | 9, 116 |
| abstract_inverted_index.trained | 87 |
| abstract_inverted_index.accuracy | 115 |
| abstract_inverted_index.adoption | 27 |
| abstract_inverted_index.balanced | 98 |
| abstract_inverted_index.encoding | 78 |
| abstract_inverted_index.ensuring | 114 |
| abstract_inverted_index.focusing | 46 |
| abstract_inverted_index.handling | 111 |
| abstract_inverted_index.improved | 163 |
| abstract_inverted_index.includes | 93 |
| abstract_inverted_index.involves | 69 |
| abstract_inverted_index.learning | 12, 30, 107 |
| abstract_inverted_index.maritime | 16, 149, 164 |
| abstract_inverted_index.masking, | 96 |
| abstract_inverted_index.proposed | 105 |
| abstract_inverted_index.accuracy, | 49, 144 |
| abstract_inverted_index.achieving | 48 |
| abstract_inverted_index.addresses | 3 |
| abstract_inverted_index.advancing | 143 |
| abstract_inverted_index.approach, | 13 |
| abstract_inverted_index.automates | 155 |
| abstract_inverted_index.datasets, | 24, 113 |
| abstract_inverted_index.detection | 5 |
| abstract_inverted_index.efficient | 80 |
| abstract_inverted_index.employing | 76 |
| abstract_inverted_index.encoding, | 95 |
| abstract_inverted_index.enhancing | 59 |
| abstract_inverted_index.objective | 35 |
| abstract_inverted_index.promising | 158 |
| abstract_inverted_index.real-time | 60, 159 |
| abstract_inverted_index.satellite | 7 |
| abstract_inverted_index.security. | 165 |
| abstract_inverted_index.showcases | 125 |
| abstract_inverted_index.training, | 45 |
| abstract_inverted_index.utilizing | 83 |
| abstract_inverted_index.Copernicus | 73 |
| abstract_inverted_index.Overcoming | 55 |
| abstract_inverted_index.Sentinel-2 | 89 |
| abstract_inverted_index.addressing | 120 |
| abstract_inverted_index.challenges | 21 |
| abstract_inverted_index.collection | 71 |
| abstract_inverted_index.detection, | 141, 157 |
| abstract_inverted_index.detection. | 54, 129 |
| abstract_inverted_index.monitoring | 61, 160 |
| abstract_inverted_index.motivating | 25 |
| abstract_inverted_index.robustness | 147 |
| abstract_inverted_index.run-length | 77, 94 |
| abstract_inverted_index.CONCLUSION: | 130 |
| abstract_inverted_index.OBJECTIVES: | 32 |
| abstract_inverted_index.Traditional | 18 |
| abstract_inverted_index.U-Net-based | 152 |
| abstract_inverted_index.algorithmic | 40, 123 |
| abstract_inverted_index.conclusion, | 132 |
| abstract_inverted_index.contributes | 135 |
| abstract_inverted_index.demonstrate | 103 |
| abstract_inverted_index.efficiency, | 50, 145 |
| abstract_inverted_index.imbalances. | 121 |
| abstract_inverted_index.limitations | 57 |
| abstract_inverted_index.methodology | 41, 68, 138 |
| abstract_inverted_index.objectives. | 65 |
| abstract_inverted_index.proficiency | 126 |
| abstract_inverted_index.techniques. | 31 |
| abstract_inverted_index.capabilities | 62 |
| abstract_inverted_index.enhancements | 161 |
| abstract_inverted_index.manipulation | 92 |
| abstract_inverted_index.successfully | 154 |
| abstract_inverted_index.INTRODUCTION: | 0 |
| abstract_inverted_index.applications. | 17, 150 |
| abstract_inverted_index.architecture, | 118 |
| abstract_inverted_index.comprehensive | 137 |
| abstract_inverted_index.effectiveness | 109 |
| abstract_inverted_index.significantly | 142 |
| abstract_inverted_index.preprocessing, | 81 |
| abstract_inverted_index.preprocessing. | 100 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 98 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile.value | 0.94430223 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |