Generalized-Capon Method for Diff-Tomo SAR Analyses of Decorrelating Scatterers

Abstract

In synthetic aperture radar (SAR) remote sensing, Differential Tomography (Diff-Tomo) is developing as a powerful crossing of the mature Differential SAR Interferometry and the emerged 3D SAR Tomography. Diff-Tomo produces advanced 4D (3D+Time) SAR imaging capabilities, extensively applied to urban deformation monitoring. More recently, it has been shown that, through Diff-Tomo, identifying temporal spectra of multiple height-distributed decorrelating scatterers, the important decorrelation-robust forest Tomography functionality is possible. To loosen application constraints of the related main experimented full model-based processing, and develop other functionalities, this work presents an adaptive, just semi-parametric, generalized-Capon Diff-Tomo method, first conceived at University of Pisa in 2013, for joint extraction of height and dynamical information of natural distributed (volumetric) scatterers, with its formalization and a series of insights. Particular reference is given to the important functionality of the separation of different decorrelation mechanisms in forest layers. Representative simulated and P-band forest data sample results are also shown. The new Diff-Tomo method is getting a flexible and rich decorrelation-robust Tomography functionality, and is able to profile height-varying temporal decorrelation, for significantly distributed scatterers.

TomoSAR Mapping of 3D Forest Structure: Contributions of L-Band Configurations

“…The first one relies on a parametric description (e.g., modeling) of the reflectivity function. Such an approach is used for instance for the generalized Capon formulations in [31] and [32], which are also related to more general covariance matching approaches [29]. The second approach does not model the reflectivity profile but constrains the solution (sub-)space of the TomoSAR inversion.…”

Three- and Four-Dimensional Topographic Measurement and Validation

“…Nonetheless, the analysis did not directly consider the impact of temporal decorrelation and weather conditions on tomographic imaging and suffered from the obvious limitation of providing data from a very limited area. In this context, the concept of extending Differential Tomography imaging for information extraction in dynamic forest layers was proposed, proper were algorithms developed [45][46][47]49], and some experimental trials were carried out by using 2-month time span P-band airborne BioSAR 1 data [45,46,49]. In particular, new tomographic algorithms were developed able to compensate for temporal effects through Differential Tomography [46,47,49], and concurrent separation of long-term temporal decorrelation mechanisms of canopy and ground was also first demonstrated at large scale [45,49].…”

“…In this context, the concept of extending Differential Tomography imaging for information extraction in dynamic forest layers was proposed, proper were algorithms developed [45][46][47]49], and some experimental trials were carried out by using 2-month time span P-band airborne BioSAR 1 data [45,46,49]. In particular, new tomographic algorithms were developed able to compensate for temporal effects through Differential Tomography [46,47,49], and concurrent separation of long-term temporal decorrelation mechanisms of canopy and ground was also first demonstrated at large scale [45,49]. Part of the activities on SAR Tomography here reported are dedicated to filling the gaps in these previous researches.…”

“…Firstly, in this paper, we present new experimental analyses to quantify the impact on temporal decorrelation on AGB retrieval by TomoSAR. Then we broaden the research by extending experimental tests of the advanced tomographic algorithms robust to temporal decorrelation [46,47,49] based on the concept of Differential Tomography [29,43]. Finally, in addition to temporal effects, we also carefully consider the need for very high resolution Tomographic imaging of natural media, showing the need for dedicated processing in the case where deep penetration occurs.…”

Accurate Despeckling and Estimation of Polarimetric Features by Means of a Spatial Decorrelation of the Noise in Complex PolSAR Data

“…Although there are many simple and effective despeckling methods available at present, it is not doubted that a great number of them, including those contained in the PolSARPro software toolbox, were devised under the assumption of spatially uncorrelated noise. Future developments will concern the use of spatially-decorrelated SLC images for SAR tomography [44], in which coherent processing makes use of the phase history. As a final consideration, the proposed procedure, that is, decorrelation followed by despeckling and/or features extimation, has no disadvantages other than computational.…”