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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...

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Main Authors:	Liu, Xinni, Kamarul Hawari, Ghazali, Han, Fengrong, Izzeldin Ibrahim, Mohamed Abdelaziz
Format:	Article
Language:	English English
Published:	Taylor and Francis Ltd. 2023
Subjects:	T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering
Online Access:	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 http://umpir.ump.edu.my/id/eprint/41822/ https://doi.org/10.1080/13682199.2023.2174651 https://doi.org/10.1080/13682199.2023.2174651
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id	my.ump.umpir.41822
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spelling	my.ump.umpir.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
institution	Universiti Malaysia Pahang Al-Sultan Abdullah
building	UMPSA Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaysia Pahang Al-Sultan Abdullah
content_source	UMPSA Institutional Repository
url_provider	http://umpir.ump.edu.my/
language	English English
topic	T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering
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
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.
format	Article
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
title	Review of CNN in aerial image processing
title_short	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_sort	review of cnn in aerial image processing
publisher	Taylor and Francis Ltd.
publishDate	2023
url	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 http://umpir.ump.edu.my/id/eprint/41822/ https://doi.org/10.1080/13682199.2023.2174651 https://doi.org/10.1080/13682199.2023.2174651
_version_	1822924591016706048
score	13.232414

Review of CNN in aerial image processing

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