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Automatic Extraction of Layover from InSAR Imagery Based on Multilayer Feature Fusion Attention Mechanism

Lookup NU author(s): Dr Jin XingORCiD

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Abstract

© 2004-2012 IEEE. Layover is a kind of geometric distortion in radar systems with side-look imaging, especially in mountainous and dense urban areas. It causes phase distortion and alters target characteristics in the acquired images, which directly hinders the application of radar images. In this letter, the multilayer feature fusion attention mechanism (MF2AM) is proposed to extract layover from interferometric synthetic aperture radar (InSAR) imagery automatically. First, the SAR image, the corresponding coherence map, and interferometric phases are channel-fused to enhance semantic information of layover areas. Then, the fused image is fed into MF2AM to extract the essential features of layover. Finally, the detection results are produced via MF2AM. MF2AM consists of the encoder and the decoder. The encoder contains three parts: the resnet101, attention-based atrous spatial pyramid (AASP), and the semantic embedding branch (SEB). In the decoder, step decoding is used to better fuse high- and low-level features and improve the effect of edge segmentation. To verify the proposed method, the images of millimeter wave InSAR system are used for the experiment, and the performance is compared with DeepLabV3+ and Geospatial Contextual Attention Mechanism (GCAM). The results show that the MF2AM has achieved obvious performance advantages. The average pixel accuracy and average intersection over union (IOU) are 0.9601 and 0.9310, respectively, and the average test time is only 7.97 s.


Publication metadata

Author(s): Cai X, Chen L, Xing J, Xing X, Luo R, Tan S, Wang J

Publication type: Article

Publication status: Published

Journal: IEEE Geoscience and Remote Sensing Letters

Year: 2022

Volume: 19

Print publication date: 23/12/2021

Online publication date: 30/08/2021

Acceptance date: 12/08/2021

ISSN (print): 1545-598X

ISSN (electronic): 1558-0571

Publisher: IEEE

URL: https://doi.org/10.1109/LGRS.2021.3105722

DOI: 10.1109/LGRS.2021.3105722


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