University of Leicester
Browse
09165883 (1).pdf (7.69 MB)

A Novel Attention Fully Convolutional Network Method for Synthetic Aperture Radar Image Segmentation

Download (7.69 MB)
journal contribution
posted on 2021-03-19, 11:04 authored by Z Yue, F Gao, Q Xiong, J Wang, A Hussain, Huiyu Zhou
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.

History

Citation

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ( Volume: 13), pp. 4585 - 4598

Author affiliation

School of Informatics

Version

  • VoR (Version of Record)

Published in

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING

Volume

13

Pagination

4585 - 4598

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

issn

1939-1404

Acceptance date

2020-08-10

Copyright date

2020

Available date

2021-03-19

Language

en

Usage metrics

    University of Leicester Publications

    Categories

    No categories selected

    Licence

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC