Zhenyu Yue
A Novel Attention Fully Convolutional Network Method for Synthetic Aperture Radar Image Segmentation
Yue, Zhenyu; Gao, Fei; Xiong, Qingxu; Wang, Jun; Hussain, Amir; Zhou, Huiyu
Abstract
As an important step of synthetic aperture radar image interpretation, synthetic aperture radar image segmentation aims at segmenting an image into different regions in terms of homogeneity. Because of the deficiency of the labeled samples and the existence of speckling noise, synthetic aperture radar image segmentation is a challenging task. We present a new method for synthetic aperture radar image segmentation in this article. Due to the large size of the original synthetic aperture radar image, we first divide the input image into small slices. Then the image slices are input to the attention-based fully convolutional network for obtaining the segmentation results. Finally, the fully connected conditional random field is adopted for improving the segmentation performance of the network. The innovations of our method are as follows: 1) The attention-based fully convolutional network is embedded with the multiscale attention network which is capable of enhancing the extraction of the image features through three strategies, namely, multiscale feature extraction, channel attention extraction, and spatial attention extraction. 2) We design a new loss function for the attention fully convolutional network by combining Lovasz-Softmax and cross-entropy losses. The new loss allows us to simultaneously optimize the intersection over union and the pixel classification accuracy of the segmentation results. The experiments are performed on two airborne synthetic aperture radar image databases. It has been proved that our method is superior to other state-of- the-art image segmentation approaches.
Journal Article Type | Article |
---|---|
Acceptance Date | Aug 9, 2020 |
Online Publication Date | Aug 12, 2020 |
Publication Date | Aug 12, 2020 |
Deposit Date | Sep 18, 2020 |
Publicly Available Date | Sep 18, 2020 |
Journal | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
Print ISSN | 1939-1404 |
Electronic ISSN | 2151-1535 |
Publisher | Institute of Electrical and Electronics Engineers |
Peer Reviewed | Peer Reviewed |
Volume | 13 |
Pages | 4585-4598 |
DOI | https://doi.org/10.1109/jstars.2020.3016064 |
Keywords | Computers in Earth Sciences; Atmospheric Science |
Public URL | http://researchrepository.napier.ac.uk/Output/2687351 |
Files
A Novel Attention Fully Convolutional Network Method For Synthetic Aperture Radar Image Segmentation
(8.1 Mb)
PDF
Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/
Technical Information
Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
You might also like
Applications of Deep Learning and Reinforcement Learning to Biological Data
(2018)
Journal Article
Guided Policy Search for Sequential Multitask Learning
(2018)
Journal Article
Learning Latent Features With Infinite Nonnegative Binary Matrix Trifactorization
(2018)
Journal Article
Cross-modality interactive attention network for multispectral pedestrian detection
(2018)
Journal Article
Downloadable Citations
About Edinburgh Napier Research Repository
Administrator e-mail: repository@napier.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search