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A novel biologically-inspired target detection method based on saliency analysis for synthetic aperture radar (SAR) imagery

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journal contribution
posted on 31.07.2020, 10:06 by Fei Ma, Fei Gao, Jun Wang, Amir Hussain, Huiyu Zhou
Saliency Object Detection (SOD) models driven by the biologically-inspired Focus of Attention (FOA) mechanism can result in highly accurate saliency maps. However, their application in high-resolution Synthetic Aperture Radar (SAR) images entails a number of intractable problems due to complex backgrounds. In this paper, we propose a novel hierarchical self-diffusion saliency (HSDS) method for detecting vehicle targets in large scale SAR images. To reduce the influence of cluttered returns on saliency analysis, we learn a weight vector from the training set to capture optimal initial saliency of the superpixels during saliency diffusion. By accounting for the multiple sizes of background objects, the saliency analysis is implemented in multi-scale space, and a saliency fusion strategy employed to integrate the multi-scale saliency maps. Simulation experiments demonstrate that our proposed method can produce a more accurate and stable detection performance, with decreased false alarms, compared to benchmark approaches.

History

Citation

Neurocomputing, Volume 402, 18 August 2020, Pages 66-79

Author affiliation

School of Informatics

Version

AM (Accepted Manuscript)

Published in

Neurocomputing

Volume

402

Pagination

66 - 79

Publisher

Elsevier

issn

0925-2312

eissn

1872-8286

Acceptance date

09/12/2019

Copyright date

2020

Available date

16/12/2020

Language

English

Publisher version

https://www.sciencedirect.com/science/article/pii/S0925231219317102