Review of CNN in aerial image processing
In recent years, deep learning algorithm has been used in many applications mainly in image processing of object detection and classification. The use of image processing techniques is becoming more interesting with the existence of drone technology with the employ of deep learning in aerial view im...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English English |
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Taylor and Francis Ltd.
2023
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| Online Access: | http://umpir.ump.edu.my/id/eprint/41822/ http://umpir.ump.edu.my/id/eprint/41822/1/Review%20of%20CNN%20in%20aerial%20image%20processing.pdf http://umpir.ump.edu.my/id/eprint/41822/2/Review%20of%20CNN%20in%20aerial%20image%20processing_ABS.pdf |
| _version_ | 1848826441908617216 |
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| author | Liu, Xinni Kamarul Hawari, Ghazali Han, Fengrong Izzeldin Ibrahim, Mohamed Abdelaziz |
| author_facet | Liu, Xinni Kamarul Hawari, Ghazali Han, Fengrong Izzeldin Ibrahim, Mohamed Abdelaziz |
| author_sort | Liu, Xinni |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | In recent years, deep learning algorithm has been used in many applications mainly in image processing of object detection and classification. The use of image processing techniques is becoming more interesting with the existence of drone technology with the employ of deep learning in aerial view image processing because of the high resolution and heaps of images taken. This paper aims to review neural networks specifically on the aerial view image by drones and to discuss the work principles and classic architectures of convolutional neural networks, its latest research trend and typical models along with target detection in object detection, image classification and semantic segmentation. In addition, this study also provided a specific application in the aerial image. Finally, the limitations of the convolutional network and expected future development trends were also discussed. Based on the findings, the deep learning algorithm was observed to provide high accuracy, it outperformed other generally image processing-based techniques. |
| first_indexed | 2025-11-15T03:44:53Z |
| format | Article |
| id | ump-41822 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T03:44:53Z |
| publishDate | 2023 |
| publisher | Taylor and Francis Ltd. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-418222024-08-30T00:09:34Z http://umpir.ump.edu.my/id/eprint/41822/ Review of CNN in aerial image processing Liu, Xinni Kamarul Hawari, Ghazali Han, Fengrong Izzeldin Ibrahim, Mohamed Abdelaziz T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering In recent years, deep learning algorithm has been used in many applications mainly in image processing of object detection and classification. The use of image processing techniques is becoming more interesting with the existence of drone technology with the employ of deep learning in aerial view image processing because of the high resolution and heaps of images taken. This paper aims to review neural networks specifically on the aerial view image by drones and to discuss the work principles and classic architectures of convolutional neural networks, its latest research trend and typical models along with target detection in object detection, image classification and semantic segmentation. In addition, this study also provided a specific application in the aerial image. Finally, the limitations of the convolutional network and expected future development trends were also discussed. Based on the findings, the deep learning algorithm was observed to provide high accuracy, it outperformed other generally image processing-based techniques. Taylor and Francis Ltd. 2023 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/41822/1/Review%20of%20CNN%20in%20aerial%20image%20processing.pdf pdf en http://umpir.ump.edu.my/id/eprint/41822/2/Review%20of%20CNN%20in%20aerial%20image%20processing_ABS.pdf Liu, Xinni and Kamarul Hawari, Ghazali and Han, Fengrong and Izzeldin Ibrahim, Mohamed Abdelaziz (2023) Review of CNN in aerial image processing. Imaging Science Journal, 71 (1). pp. 1-13. ISSN 1368-2199. (Published) https://doi.org/10.1080/13682199.2023.2174651 https://doi.org/10.1080/13682199.2023.2174651 |
| spellingShingle | T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Liu, Xinni Kamarul Hawari, Ghazali Han, Fengrong Izzeldin Ibrahim, Mohamed Abdelaziz Review of CNN in aerial image processing |
| title | Review of CNN in aerial image processing |
| title_full | Review of CNN in aerial image processing |
| title_fullStr | Review of CNN in aerial image processing |
| title_full_unstemmed | Review of CNN in aerial image processing |
| title_short | Review of CNN in aerial image processing |
| title_sort | review of cnn in aerial image processing |
| topic | T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/41822/ http://umpir.ump.edu.my/id/eprint/41822/ http://umpir.ump.edu.my/id/eprint/41822/ http://umpir.ump.edu.my/id/eprint/41822/1/Review%20of%20CNN%20in%20aerial%20image%20processing.pdf http://umpir.ump.edu.my/id/eprint/41822/2/Review%20of%20CNN%20in%20aerial%20image%20processing_ABS.pdf |