Search

In our previous studies, we demonstrated the usefulness of TanDEM-X interferometric bistatic mode with single polarization to obtain forest heights for the purposes of large area mapping. A key feature of our approach has been the use of a simplified Random Volume Over Ground (RVOG) model that locally estimates forest height. The model takes TanDEM-X interferometric coherence amplitude as an input and uses an external Digital Surface Model (DSM) to account for local slope variations due to terrain topography in order to achieve accurate forest height estimation. The selection of DSM for use as a local slope reference is essential, as an inaccurate DSM will result in less accurate terrain-correction and forest height estimation. In this paper, we assessed TanDEM-X height estimates associated with scale variations in different DSMs used in the model over a remote sensing supersite in Petawawa, Canada. The DSMs used for assessments and comparisons included ASTER GDEM, ALOS GDSM, airborne DRAPE DSM, Canadian DSM and TanDEM-X DSM. Airborne Laser Scanning (ALS) data were used as reference for terrain slope and forest height comparisons. The results showed that, with the exception of the ASTER GDEM, all DSMs were sufficiently accurate for the simplified RVOG model to provide a satisfactory estimate of stand-level forest height. When compared to the ALS 95th height percentile, the modeled forest heights had R2 values greater than 80% and Root-Mean-Square Errors (RMSE) less than 2 m. For a close similarity in slope estimation with the ALS reference, coverage across Canada and open data access, the 0.75 arc-second (20 m) resolution Canadian DSM was selected as a preferred choice for the simplified RVOG model to provide TanDEM-X height estimation in Canada.

The performance of quantitative remote sensing based on multidimensional synthetic aperture radars (SARs), and polarimetric SAR systems in particular, depends strongly on a correct statistical characterization of the data, i.e., on a complete knowledge of the effects of the speckle noise. In this framework, the eigendecompostion of the covariance or coherency matrices and the associated H/[alpha].[bar]/A decomposition have demonstrated the potential for quantitative estimation of physical parameters. In this paper, we present a detailed study of the statistics associated with this decomposition. This analysis requires the introduction of mathematical tools that are not well known in the remote sensing community. For this reason, we include a review section to present them. Using this work, we then present an expression for the probability density function of the sample eigenvalues of the covariance or coherency matrix. The availability of this expression allows a complete study of the separated sample eigenvalues, as well as, the entropy H and the anisotropy A. As demonstrated, all these parameters must be considered as asymptotically nonbiased with respect to the number of looks. In order to reduce the biases for a small number of averaged samples, a novel estimator for the eigenvalues is proposed. The results of this work are analyzed by means of simulated and real airborne SAR data. This analysis permits us to determine in detail the effects of the number of averaged samples in the estimation of physical information in radar polarimetry. Index Terms--Eigendecomposition, estimation theory, polarimetry, speckle, synthetic aperture radar (SAR), target decomposition theorems.

Recently, polarimetric synthetic aperture radar (SAR) interferometry has generated much interest for forest applications. Forest heights and ground topography can be extracted based on interferometric coherence using a random volume over ground coherent mixture model. The coherence estimation is of paramount importance for the accuracy of forest height estimation. The coherence (or correlation coefficient) is a statistical average of neighboring pixels of similar scattering characteristics. The commonly used algorithm is the boxcar filter, which has the deficiency of indiscriminate averaging of neighboring pixels. The result is that coherence values are lower than they should be. In this paper, we propose a new algorithm to improve the accuracy in the coherence estimation based on speckle filtering of the 6 x 6 polarimetric interferometry matrix. Simulated images are used to verify the effectiveness of this adaptive algorithm. German Aerospace Center (DLR) L-Band E-SAR data are applied to demonstrate the improved accuracy in coherence and in forest height estimation. Index Terms--Coherence estimation, forest parameter inversion, polarimetric interferometry, synthetic aperture radar (SAR) speckle filtering.

This paper proposes a new model for the inversion of surface roughness and soil moisture from polarimetric synthetic aperture radar (SAR) data, based on the eigenvalues and eigenvectors of the polarimetric coherency matrix. It demonstrates how three polarimetric parameters, namely the scattering entropy (H), the scattering anisotropy (A), and the alpha angle ([alpha]) may be used in order to decouple surface roughness from moisture content estimation offering the possibility of a straightforward inversion of these two surface parameters. The potential of the proposed inversion algorithm is investigated using fully polarimetric laboratory measurements as well as airborne L-band SAR data and ground measurements from two different test sites in Germany, the Elbe-Auen site and the Weiherbach site. Index Terms--Inversion, soil moisture, surface parameters, surface roughness, synthetic aperture radar (SAR) polarimetry.

The objective of this paper is to examine the application of single-baseline polarimetric SAR interferometry to the remote sensing and measurement of structure over forested terrain. For this, a polarimetric coherent scattering model for vegetation cover suitable for the estimation of forest parameters from interferometric observables is introduced, discussed and validated. Based on this model, an inversion algorithm which allows the estimation of forest parameters such as tree height, average extinction, and underlying topography from single-baseline fully polarimetric interferometric data is addressed. The performance of the inversion algorithm is demonstrated using fully polarimetric single baseline experimental data acquired by DLR's E-SAR system at L-band. Index Terms--Forest parameter inversion, polarimetric interferometry, radar polarimetry, synthetic aperture radar (SAR) interferometry.

Polarimetric radar interferometry is much more sensitive to the distribution of oriented objects in a vegetated land surface than either polarimetry or interferometry alone. This paper shows that single-baseline polarimetric interferometry can be used to estimate the heights of oriented-vegetation volumes and underlying topography, while at least two baselines are needed for randomly oriented volumes. Single-baseline, calculated vegetation-height accuracies are in the range of 2-8 m for reasonable levels of vegetation orientation in forest canopies. Index terms - Polarimetric interferometry, radar interferometry, radar polarimetry, vegetation remote sensing.

In this paper, we show how the entropy-alpha target decomposition scheme may be used for parametric inversion studies on random particle cloud models for vegetation layers. The decomposition is detailed first and then applied to a two-parameter model for backscatter from a random cloud of small anisotropic particles. The two main parameters used are the mean particle shape and the mean orientation angle of the cloud. An inversion algorithm is presented and applied to broad-band polarimetric radar data from the European Microwave Signature Laboratory (EMSL), Joint Research Center, Ispra, Italy. The results have been obtained from measurements of a fir tree and a ficus tree. They show a wavelength scale dependence of the shape and distribution of scatterers, which reflects the complex volume scattering nature of such problems. Moreover, the values and trends from these two trees as a function of the frequency are different, as expected from their physical structures. Consequently, this algorithm has the potential to be useful in the construction of classification schemes for vegetation. Index Terms: Inverse problems, radar polarimetry, synthetic aperture radar (SAR).

In this paper, we propose a new method for unsupervised classification of terrain types and man-made objects using polarimetric synthetic aperture radar (SAR) data. This technique is a combination of the unsupervised classification based on polarimetric target decomposition [9] and the maximum likelihood classifier based on the complex Wishart distribution for the polarimetric covariance matrix [15]. We use Cloude and Pottier's method to initially classify the polarimetric SAR image. The initial classification map defines training sets for classification based on the Wishart distribution. The classified results are then used to define training sets for the next iteration. Significant improvement has been observed in iteration. The iteration ends when the number of pixels switching classes becomes smaller than a predetermined number or when other criteria are met. We observed that the class centers in the entropy-alpha plane are shifted by each iteration. The final class centers in the entropy-alpha plane are useful for class identification by the scattering mechanism associated with each zone. The advantages of this method are the automated classification, and the interpretation of each class based on scattering mechanism. The effectiveness of this algorithm is demonstrated using a JPL/AIRSAR polarimetric SAR image. Index Terms: Radar polarimetry, synthetic aperture radar, terrain classification

By using the first-order iterative solution of vector radiative transfer in small albedo for a layer of nonuniformly-oriented, random, nonspherical scatterers, the Mueller matrix and coherency matrix are obtained. Eigenanalysis of the de coherency matrix is numerically calculated. The eigenanalysis of the coherency matrix is proposed as a better method for identifying physical scattering mechanisms than direct inspection of the Mueller matrix. The functional dependence of polarimetric scattering is also discussed.

We investigate multichannel imaging radar systems employing coherent combinations of polarimetry and interferometry (Pol-InSAR). Such systems are well suited for the extraction of bio- and geophysical parameters by evaluating the combined scattering from surfaces and volumes. This combination leads to several important differences between the design of Pol-InSAR sensors and conventional single polarisation SAR interferometers. We first highlight these differences and then investigate the Pol-InSAR performance of two proposed spaceborne SAR systems (ALOS/PalSAR and TerraSAR-L) operating in repeat-pass mode. For this, we introduce the novel concept of a phase tube which enables (1) a quantitative assessment of the Pol-InSAR performance, (2) a comparison between different sensor configurations, and (3) an optimization of the instrument settings for different Pol-InSAR applications. The phase tube may hence serve as an interface between system engineers and application-oriented scientists. The performance analysis reveals major limitations for even moderate levels of temporal decorrelation. Such deteriorations may be avoided in single-pass sensor configurations and we demonstrate the potential benefits from the use of future bi- and multistatic SAR interferometers.