Model-Based Target Scattering Decomposition of Polarimetric SAR Tomography

Aghababaee, Hossein; Sahebi, Mahmod Reza

doi:10.1109/tgrs.2017.2757062

Cited by 31 publications

(15 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…. ..,z M ) with length of M v = M−M g indicate the object and wavelet domain intervals, respectively; whereas z g is a priori known ground height that can be estimated using the techniques introduced in [15,20] or from the external source of information. Finally, the modeled polarimetric MB covariance matrix R p in Equation (5) is represented by:…”

Section: Hybrid Sparse Based Full-rank Reconstructionmentioning

confidence: 99%

Sparsity Based Full Rank Polarimetric Reconstruction of Coherence Matrix T

et al. 2019

Self Cite

View full text Add to dashboard Cite

In the frame of polarimetric synthetic aperture radar (SAR) tomography, full-ranks reconstruction framework has been recognized as a significant technique for fully characterization of superimposed scatterers in a resolution cell. The technique, mainly is characterized by the advantages of polarimetric scattering pattern reconstruction, allows physical feature extraction of the scatterers. In this paper, to overcome the limitations of conventional full-rank tomographic techniques in natural environments, a polarimetric estimator with advantages of super-resolution imaging is proposed. Under the frame of compressive sensing (CS) and sparsity based reconstruction, the profile of second order polarimetric coherence matrix T is recovered. Once the polarimetric coherence matrices of the scatterers are available, the physical features can be extracted using classical polarimetric processing techniques. The objective of this study is to evaluate the performance of the proposed full-rank polarimetric reconstruction by means of conventional three-component decomposition of T, and focusing on the consistency of vertical resolution and polarimetric scattering pattern of the scatterers. The outcomes from simulated and two different real data sets confirm that significant improvement can be achieved in the reconstruction quality with respect to conventional approaches.

show abstract

Section: Hybrid Sparse Based Full-rank Reconstructionmentioning

confidence: 99%

Sparsity Based Full Rank Polarimetric Reconstruction of Coherence Matrix T

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…Synthetic aperture radar tomography (TomoSAR) is a suitable candidate to solve this problem because of its three-dimensional imaging capability [6][7][8][9]. TomoSAR forms an additional aperture along the vertical direction, which can discriminate scatterers in different heights in the same resolution cell.…”

Section: Introductionmentioning

confidence: 99%

Underlying Topography Inversion Using Dual Polarimetric TomoSAR

Peng

Long

Wang

et al. 2021

Sensors

View full text Add to dashboard Cite

Underlying topography plays an important role in the national economic construction, military security, resource exploration and investigation. Since synthetic aperture radar tomography (TomoSAR) can achieve the three-dimensional imaging of forests, it has been widely used in underlying topography estimation. At present, there are two kinds of TomoSAR based on the applied datasets: single polarimetric TomoSAR (SP-TomoSAR) and fully polarimetric TomoSAR (FP-TomoSAR). However, SP-TomoSAR cannot obtain the underlying topography accurately due to the lack of enough observations. FP-TomoSAR can improve the estimation accuracy of underlying topography. However, it requires high-cost data acquisition for the large-scale application. Thus, this paper proposes the dual polarimetric TomoSAR (DP-TomoSAR) as another suitable candidate to estimate the underlying topography because of its wide swath and multiple polarimetric observations. Moreover, three frequently used spectral estimation algorithms, namely, Beamforming, Capon and MUSIC, are used in DP-TomoSAR. For validation, a series of simulated experiments was carried out, and the airborne P-band multiple polarimetric SAR data over the Lope, Gabon was also acquired to estimate the underlying topography. The results suggest that DP-TomoSAR in HH & HV combination is more suitable to estimate underlying topography over forest areas than other DP combinations. Moreover, the estimation accuracy of DP-TomoSAR is slightly lower than that of FP-TomoSAR but is higher than that of SP-TomoSAR.

show abstract

“…Synthetic-aperture radar (SAR) tomography is a three-dimensional SAR imaging technique, which can provide an estimation of scatterers' distribution along some height direction and generate three-dimensional images of earth surface. The SAR tomography has been receiving a steadily growing interest in the recent years [1][2][3]. However, this tomography approach requires a synthetic aperture along the height direction, which is often formed by using several multi-pass acquisitions in the same imaging scene.…”

Section: Introductionmentioning

confidence: 99%

Bayesian compressive sensing for synthetic-aperture radar tomography imaging

Ren¹,

Qin²,

Qiao³

2020

Ukr. J. Phys. Opt.

View full text Add to dashboard Cite

To achieve high-resolution three-dimensional images, a number of imaging methods based on compressive sensing (CS) have been suggested in the recent years for synthetic-aperture radar (SAR) tomography. However, the CS-based methods are sensitive to noise. In this work, we develop a new Bayesian compressive sensing (BCS) imaging method for the SAR tomography. In the framework of BCS, a 'sparseness' prior distribution of the imaging scene and an additive noise are properly considered in the imaging process. As a consequence, the BCS-based method under the conditions of low noise levels can provide a better performance than the common norm-based CS methods. The results obtained via simulations of our SAR-tomography imaging method confirm its advantages.

show abstract

Model-Based Target Scattering Decomposition of Polarimetric SAR Tomography

Cited by 31 publications

References 32 publications

Sparsity Based Full Rank Polarimetric Reconstruction of Coherence Matrix T

Sparsity Based Full Rank Polarimetric Reconstruction of Coherence Matrix T

Underlying Topography Inversion Using Dual Polarimetric TomoSAR

Bayesian compressive sensing for synthetic-aperture radar tomography imaging

Contact Info

Product

Resources

About