Reconstruction of synthetic aperture radar data using hybrid compressive sensing and deep neural network algorithm

The reconstruction of reflectivity profile of the synthetic aperture radar (SAR) is an important field of research. SAR tomography is an advanced 3D imaging technique for the spectrum estimation in the elevation direction for each azimuth resolution cell. This work presents the processing chain for...

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Vydané v:International journal of communication systems Ročník 37; číslo 6
Hlavní autori: Paramasivam, Saranya, Kaliyaperumal, Vani
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Chichester Wiley Subscription Services, Inc 01.04.2024
Predmet:
Algorithms
Artificial neural networks
Deep learning
Errors
Image reconstruction
Imaging techniques
Machine learning
Radar data
Radar imaging
Resolution cell
Synthetic aperture radar
ISSN:1074-5351, 1099-1131
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Shrnutí:The reconstruction of reflectivity profile of the synthetic aperture radar (SAR) is an important field of research. SAR tomography is an advanced 3D imaging technique for the spectrum estimation in the elevation direction for each azimuth resolution cell. This work presents the processing chain for the tomographic reconstruction from ALOS PALSAR data for an urban region. First, the data are preprocessed by removing the speckle noise followed by atmospheric phase screen and topographic correction. Then the SAR images are stacked together with one master image and the remaining slave images on the baseline value. After the images are coregistered, the interferogram is generated from the image to obtain the difference of the phase value. Then the proposed super resolution SAR (SRS) algorithm is attempted for TomoSAR processing, which combines the functionality of modern machine learning method like deep learning with parametric block‐based compressive sensing approach. Finally, a 3D image is reconstructed from the input data. Evaluation is carried out by comparing the results of the proposed method with other spectrum estimation methods such as nonlinear least square, Capon, and multisignal classification. The normal baseline of the interferometric fringes is about 368.54 m. The proposed SRS algorithm gives improved results with less mean elevation error of 1.8 m and the less standard deviation error of 4.85 m. Finally, the result reveals that the SRS algorithm performed better than other TomoSAR algorithms with the less relative error 0.003.
Bibliografia:ObjectType-Article-1
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content type line 14
ISSN:1074-5351
1099-1131
DOI:10.1002/dac.5703