A Virtual Adaptive Beamforming Approach for Feature Enhanced SAR Tomography
Synthetic aperture radar (SAR) tomography (TomoSAR) is a remote sensing technique that allows for the 3-D representation of the illuminated areas, recovering the vertical distribution of the backscattered power at each range-azimuth position. In this context, and with the aim of retrieving feature-e...
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ftdlr:oai:elib.dlr.de:127466 2024-05-19T07:46:07+00:00 A Virtual Adaptive Beamforming Approach for Feature Enhanced SAR Tomography Martin del Campo Becerra, Gustavo Reigber, Andreas Nannini, Matteo 2019-08 https://elib.dlr.de/127466/ unknown Martin del Campo Becerra, Gustavo und Reigber, Andreas und Nannini, Matteo (2019) A Virtual Adaptive Beamforming Approach for Feature Enhanced SAR Tomography. In: International Geoscience and Remote Sensing Symposium (IGARSS). IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2019-07-28 - 2019-08-02, Yokohama, Japan. doi:10.1109/igarss.2019.8898113 <https://doi.org/10.1109/igarss.2019.8898113>. SAR-Technologie Konferenzbeitrag PeerReviewed 2019 ftdlr https://doi.org/10.1109/igarss.2019.8898113 2024-04-25T00:50:07Z Synthetic aperture radar (SAR) tomography (TomoSAR) is a remote sensing technique that allows for the 3-D representation of the illuminated areas, recovering the vertical distribution of the backscattered power at each range-azimuth position. In this context, and with the aim of retrieving feature-enhanced tomograms, this paper addresses a new multi-stage iterative method that operates robustly in real-world TomoSAR operating scenarios with irregularly distributed acquisition constellations and only few available looks. The addressed approach alleviates the drawbacks of the conventional matched spatial filter (MSF) technique, which retrieves high ambiguity levels when irregular sampling is considered. Also, it alleviates the drawbacks of the commonly used Capon beamforming technique, which results to be inapplicable when the involved data covariance (structure) matrices are rank deficient. The addressed novel method combines the descriptive experiment design regularization (DEDR) framework for enhanced image reconstruction, with the wavelet domain thresholding (WDT)-based sparsity promoting refinement in the wavelet transform (WT) domain. The capabilities of the addressed WDT-refined virtual adaptive beamforming (VAB) approach, which we refer to as WAVAB, are corroborated via processing P-band airborne TomoSAR data of the German Aerospace Center (DLR), acquired by the E-SAR system over the test site located at the Vindeln municipality, northern Sweden, in 2008. Conference Object Northern Sweden German Aerospace Center: elib - DLR electronic library IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium 1132 1135 |
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German Aerospace Center: elib - DLR electronic library |
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SAR-Technologie |
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SAR-Technologie Martin del Campo Becerra, Gustavo Reigber, Andreas Nannini, Matteo A Virtual Adaptive Beamforming Approach for Feature Enhanced SAR Tomography |
topic_facet |
SAR-Technologie |
description |
Synthetic aperture radar (SAR) tomography (TomoSAR) is a remote sensing technique that allows for the 3-D representation of the illuminated areas, recovering the vertical distribution of the backscattered power at each range-azimuth position. In this context, and with the aim of retrieving feature-enhanced tomograms, this paper addresses a new multi-stage iterative method that operates robustly in real-world TomoSAR operating scenarios with irregularly distributed acquisition constellations and only few available looks. The addressed approach alleviates the drawbacks of the conventional matched spatial filter (MSF) technique, which retrieves high ambiguity levels when irregular sampling is considered. Also, it alleviates the drawbacks of the commonly used Capon beamforming technique, which results to be inapplicable when the involved data covariance (structure) matrices are rank deficient. The addressed novel method combines the descriptive experiment design regularization (DEDR) framework for enhanced image reconstruction, with the wavelet domain thresholding (WDT)-based sparsity promoting refinement in the wavelet transform (WT) domain. The capabilities of the addressed WDT-refined virtual adaptive beamforming (VAB) approach, which we refer to as WAVAB, are corroborated via processing P-band airborne TomoSAR data of the German Aerospace Center (DLR), acquired by the E-SAR system over the test site located at the Vindeln municipality, northern Sweden, in 2008. |
format |
Conference Object |
author |
Martin del Campo Becerra, Gustavo Reigber, Andreas Nannini, Matteo |
author_facet |
Martin del Campo Becerra, Gustavo Reigber, Andreas Nannini, Matteo |
author_sort |
Martin del Campo Becerra, Gustavo |
title |
A Virtual Adaptive Beamforming Approach for Feature Enhanced SAR Tomography |
title_short |
A Virtual Adaptive Beamforming Approach for Feature Enhanced SAR Tomography |
title_full |
A Virtual Adaptive Beamforming Approach for Feature Enhanced SAR Tomography |
title_fullStr |
A Virtual Adaptive Beamforming Approach for Feature Enhanced SAR Tomography |
title_full_unstemmed |
A Virtual Adaptive Beamforming Approach for Feature Enhanced SAR Tomography |
title_sort |
virtual adaptive beamforming approach for feature enhanced sar tomography |
publishDate |
2019 |
url |
https://elib.dlr.de/127466/ |
genre |
Northern Sweden |
genre_facet |
Northern Sweden |
op_relation |
Martin del Campo Becerra, Gustavo und Reigber, Andreas und Nannini, Matteo (2019) A Virtual Adaptive Beamforming Approach for Feature Enhanced SAR Tomography. In: International Geoscience and Remote Sensing Symposium (IGARSS). IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2019-07-28 - 2019-08-02, Yokohama, Japan. doi:10.1109/igarss.2019.8898113 <https://doi.org/10.1109/igarss.2019.8898113>. |
op_doi |
https://doi.org/10.1109/igarss.2019.8898113 |
container_title |
IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium |
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