SFRE-Net: Scattering Feature Relation Enhancement Network for Aircraft Detection in SAR Images
Aircraft detection in synthetic aperture radar (SAR) images is a challenging task due to the discreteness of aircraft scattering characteristics, the diversity of aircraft size, and the interference of complex backgrounds. To address these problems, we propose a novel scattering feature relation enh...
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Published in | Remote sensing (Basel, Switzerland) Vol. 14; no. 9; p. 2076 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
Basel
MDPI AG
01.05.2022
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Subjects | |
Online Access | Get full text |
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Summary: | Aircraft detection in synthetic aperture radar (SAR) images is a challenging task due to the discreteness of aircraft scattering characteristics, the diversity of aircraft size, and the interference of complex backgrounds. To address these problems, we propose a novel scattering feature relation enhancement network (SFRE-Net) in this paper. Firstly, a cascade transformer block (TRsB) structure is adopted to improve the integrity of aircraft detection results by modeling the correlation between feature points. Secondly, a feature-adaptive fusion pyramid structure (FAFP) is proposed to aggregate features of different levels and scales, enable the network to autonomously extract useful semantic information, and improve the multi-scale representation ability of the network. Thirdly, a context attention-enhancement module (CAEM) is designed to improve the positioning accuracy in complex backgrounds. Considering the discreteness of scattering characteristics, the module uses a dilated convolution pyramid structure to improve the receptive field and then captures the position of the aircraft target through the coordinate attention mechanism. Experiments on the Gaofen-3 dataset demonstrate the effectiveness of SFRE-Net with a precision rate of 94.4% and a recall rate of 94.5%. |
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ISSN: | 2072-4292 2072-4292 |
DOI: | 10.3390/rs14092076 |