Semantic segmentation of aerial images using U-net architecture
Joint Authors
Husayn, Sarah kamil
Ali, Khawlah Husayn
Source
The Iraqi Journal of Electrical and Electronic Engineering
Issue
Vol. 18, Issue 1 (30 Jun. 2022), pp.58-63, 6 p.
Publisher
University of Basrah College of Engineering
Publication Date
2022-06-30
Country of Publication
Iraq
No. of Pages
6
Main Subjects
Information Technology and Computer Science
Topics
Abstract EN
Arial images are very high resolution.
the automation for map generation and semantic segmentation of aerial images are challenging problems in semantic segmentation.
the semantic segmentation process does not give us precise details of the remote sensing images due to the low resolution of the aerial images.
hence, we propose an algorithm u-net architecture to solve this problem.
it is classified into two paths.
the compression path (also called : the encoder) is the first path and is used to capture the image's context.
the encoder is just a convolutional and maximal pooling layer stack.
the symmetric expanding path (also called : the decoder) is the second path, which is used to enable exact localization by transposed convolutions.
this task is commonly referred to as dense prediction, which is completely connected to each other and also with the former neurons which gives rise to dense layers.
thus it is an end-to-end fully convolutional network (FCN), i.
e.
it only contains convolutional layers and does not contain any dense layer because of which it can accept images of any size.
the performance of the model will be evaluated by improving the image using the proposed method u-net and obtaining an improved image by measuring the accuracy compared with the value of accuracy with previous methods.
American Psychological Association (APA)
Husayn, Sarah kamil& Ali, Khawlah Husayn. 2022. Semantic segmentation of aerial images using U-net architecture. The Iraqi Journal of Electrical and Electronic Engineering،Vol. 18, no. 1, pp.58-63.
https://search.emarefa.net/detail/BIM-1380209
Modern Language Association (MLA)
Husayn, Sarah kamil& Ali, Khawlah Husayn. Semantic segmentation of aerial images using U-net architecture. The Iraqi Journal of Electrical and Electronic Engineering Vol. 18, no. 1 (Jun. 2022), pp.58-63.
https://search.emarefa.net/detail/BIM-1380209
American Medical Association (AMA)
Husayn, Sarah kamil& Ali, Khawlah Husayn. Semantic segmentation of aerial images using U-net architecture. The Iraqi Journal of Electrical and Electronic Engineering. 2022. Vol. 18, no. 1, pp.58-63.
https://search.emarefa.net/detail/BIM-1380209
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 63
Record ID
BIM-1380209