Multidimensional SAR tomography: Methods and applications

Lombardini, Fabrizio; Tebaldini, Stefano

doi:10.1109/igarss.2017.8127491

Cited by 3 publications

(1 citation statement)

References 28 publications

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“…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.

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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.

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Section: Introductionmentioning

confidence: 99%

Bayesian compressive sensing for synthetic-aperture radar tomography imaging

Ren¹,

Qin²,

Qiao³

2020

Ukr. J. Phys. Opt.

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Persistent Scatterer Detection and 3-D Reconstruction of Transmission Tower in Mountain Area Based on SAR Tomography

Chen

et al. 2020

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium

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First Demonstration of Space-Borne Polarization Coherence Tomography for Characterizing Hyrcanian Forest Structural Diversity

et al. 2023

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Structural diversity is recognized as a complementary aspect of biological diversity and plays a fundamental role in forest management, conservation, and restoration. Hence, the assessment of structural diversity has become a major effort in the primary international processes, dealing with biodiversity and sustainable forest management. Because of prohibitive costs associated with the ground measurements of forest structure, despite their high accuracy, space-borne polarization coherence tomography (PCT) can introduce an alternative approach given its ability to provide a vertical reflectivity profile and spatiotemporal resolutions related to detecting forest structural changes. In this study, for the first time ever, the potential of space-borne PCT was evaluated in a broad-leaved Hyrcanian forest of Iran over 308 circular sample plots with an area of 0.1 ha. Two aspects of horizontal structure diversity, including standard deviation of diameter at breast height (σdbh) and the number of trees (N), were predicted as important characteristics in wood production and biomass estimation. In addition, the performance of prediction algorithms, including multiple linear regression (MLR), k-nearest neighbors (k-NN), random forest (RF), and support vector regression (SVR) were compared. We addressed the issue of temporal decorrelation in space-borne PCT utilizing the single-pass TanDEM-X interferometer. The data were acquired in standard DEM mode with single polarization of HH. Consequently, airborne laser scanning (ALS) was used to estimate initial values of height hv and ground phase φ0. The Fourier–Legendre series was used to approximate the relative reflectivity profile of each pixel. To link the relative reflectivity profile averaged within each plot with corresponding ground measurements of σdbh and N, thirteen geometrical and physical parameters were defined (P1−P13). Leave-one-out cross validation (LOOCV) showed a better performance of k-NN than the other algorithms in predicting σdbh and N. It resulted in a relative root mean square error (rRMSE) of 32.80%, mean absolute error (MAE) of 4.69 cm, and R2* of 0.25 for σdbh, whereas only 22% of the variation in N was explained using the PCT algorithm with an rRMSE of 41.56%. This study revealed promising results utilizing TanDEM-X data even though the accuracy is still limited. Hence, an entire assessment of the used framework in characterizing the reflectivity profile and the possible effect of the scale is necessary for future studies.

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Multidimensional SAR tomography: Methods and applications

Cited by 3 publications

References 28 publications

Bayesian compressive sensing for synthetic-aperture radar tomography imaging

Bayesian compressive sensing for synthetic-aperture radar tomography imaging

Persistent Scatterer Detection and 3-D Reconstruction of Transmission Tower in Mountain Area Based on SAR Tomography

First Demonstration of Space-Borne Polarization Coherence Tomography for Characterizing Hyrcanian Forest Structural Diversity

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