Aircraft Detection from Low SCNR SAR Imagery Using Coherent Scattering Enhancement and Fused Attention Pyramid

Author:

Zhang Xinzheng12ORCID,Hu Dong1,Li Sheng3,Luo Yuqing1,Li Jinlin1,Zhang Ce45ORCID

Affiliation:

1. School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China

2. Chongqing Key Laboratory of Space Information Network and Intelligent Information Fusion, Chongqing 400044, China

3. Science and Technology on Electromagnetic Scattering Laboratory, Beijing 100854, China

4. Lancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, UK

5. UK Centre for Ecology & Hydrology, Library Avenue, Lancaster LA1 4AP, UK

Abstract

Recently, methods based on deep learning have been applied to target detection using synthetic aperture radar (SAR) images. However, due to the SAR imaging mechanism and low signal-clutter-noise-ratio (SCNR), it is still a challenging task to perform aircraft detection using SAR imagery. To address this issue, a novel aircraft detection method is proposed for low SCNR SAR images that is based on coherent scattering enhancement and a fusion attention mechanism. Considering the scattering characteristics discrepancy between human-made targets and natural background, a coherent scattering enhancement technique is introduced to heighten the aircraft scatter information and suppress the clutter and speckle noise. This is beneficial for the later ability of the deep neural network to extract accurate and discriminative semantic information about the aircraft. Further, an improved Faster R-CNN is developed with a novel pyramid network constructed by fusing local and contextual attention. The local attention adaptively highlights the significant objects by enhancing their distinguishable features, and the contextual attention facilitates the network to extract distinct contextual information of the image. Fusing the local and contextual attention can guarantee that the aircraft is detected as completely as possible. Extensive experiments are performed on TerraSAR-X SAR datasets for benchmark comparison. The experimental results demonstrate that the proposed aircraft detection approach could achieve up to 91.7% of average precision in low SCNR, showing effectiveness and superiority over a number of benchmarks.

Funder

National Natural Science Foundation of China

Natural Science Foundation of Chongqing

Natural Environment Research Council

Publisher

MDPI AG

Subject

General Earth and Planetary Sciences

Reference49 articles.

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3. Ground moving target detection and trajectory reconstruction methods for multichannel airborne circular SAR;Ge;IEEE Trans. Aerosp. Electron. Syst.,2022

4. Geometrical features-based method for aircraft target interpretation in high-resolution SAR images;Gao;Foreign Electron. Meas. Technol.,2022

5. Aircraft target detection from spaceborne synthetic aperture radar image;Guo;Aerosp. Shanghai,2018

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