Multibaseline polarimetric synthetic aperture radar tomography of forested areas using wavelet-based distribution compressive sensing

Abstract

Abstract. The three-dimensional (3-D) structure of forests, especially the vertical structure, is an important parameter of forest ecosystem modeling for monitoring ecological change. Synthetic aperture radar tomography (TomoSAR) provides scene reflectivity estimation of vegetation along elevation coordinates. Due to the advantages of super-resolution imaging and a small number of measurements, distribution compressive sensing (DCS) inversion techniques for polarimetric SAR tomography were successfully developed and applied. This paper addresses the 3-D imaging of forested areas based on the framework of DCS using fully polarimetric (FP) multibaseline SAR interferometric (MB-InSAR) tomography at the P-band. A new DCS-based FP TomoSAR method is proposed: a new wavelet-based distributed compressive sensing FP TomoSAR method (FP-WDCS TomoSAR method). The method takes advantage of the joint sparsity between polarimetric channel signals in the wavelet domain to jointly inverse the reflectivity profiles in each channel. The method not only allows high accuracy and super-resolution imaging with a low number of acquisitions, but can also obtain the polarization information of the vertical structure of forested areas. The effectiveness of the techniques for polarimetric SAR tomography is demonstrated using FP P-band airborne datasets acquired by the ONERA SETHI airborne system over a test site in Paracou, French Guiana. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

SPICE-Based SAR Tomography over Forest Areas Using a Small Number of P-Band Airborne F-SAR Images Characterized by Non-Uniformly Distributed Baselines

“…Considering the implementation facility and compactness to solve the L 1 -norm minimization [23][24][25][26][27][28][29][30], this paper applies the CVX solver for the CS TomoSAR method. The hyperparameter is set as 1000, which is an appropriate choice.…”

“…(1) For a small number of acquisitions, the common Fourier-based TomoSAR focusing approaches bring about some imaging quality problems [22,23]. If we want to get a good tomographic focusing, we have to acquire more images.…”

“…In order to solve this problem, compressive sensing (CS)-based TomoSAR has been proposed [22][23][24][25][26][27][28][29][30][31]. CS, as a sparse estimation technique, breaks the limitation of Shannon's sampling theorem when the signal is compressible or sparse in a transform domain [22][23][24][25][26][27][28]. It can not only greatly reduce the cost of the data acquisition, but also effectively overcomes the limitation of non-uniformly distributed baselines.…”

Underlying Topography Inversion Using TomoSAR Based on Non-Local Means for an L-Band Airborne Dataset

“…In other words, TomoSAR can be used to obtain the scattering echo along the elevation by the computed tomography for each resolution cell [14]. Therefore, it has been used for underlying topography inversion by effectively separating different scatterers along the vertical direction in the same resolution cell [15][16][17][18].…”

Comparision of Methods for Polarimetric SAR Tomography with Small Number of Baselines in Forested Areas

“…Moreover, a sparse reconstruction technique, Compressive sensing (CS) [7][8][9][10][11] , has been used for SAR tomography. It aims at using a small number of acquisitions to construct the 3-D image with superresolution power under the condition that the reflectivity signal is sparse along the elevation direction [12][13][14][15][16][17][18][19][20][21][22] .…”