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Measuring the vertical structure of tropical forests using remote sensing technology is challenging. To overcome this, active sensors, such as P-band synthetic aperture radar (SAR) and light detection and ranging (LiDAR), are used to penetrate thick vegetation layers. NASA’s global ecosystem dynamics investigation (GEDI) uses spaceborne LiDAR data. In contrast, the European Space Agency’s (ESA) BIOMASS mission uses multiple acquisitions of SAR data to create 3-D images through a technique called SAR tomography (TomoSAR). This letter discusses the forest’s vertical structure, such as volume peak (or volume scattering center), penetration, and reflectivity, using GEDI and airborne P-band TomoSAR by analyzing measurements at tropical forest sites in South America and Africa. It was found that the location of the volume peak in TomoSAR is consistently lower than in GEDI, with a range of 2–4 m depending on the polarization and the height of the forest layers. Compared with GEDI, TomoSAR data have a better ground reflection for vegetation taller than 25 m. GEDI and TomoSAR data can accurately capture vertical information in the canopy levels (between 10 and 40 m), displaying a strong correlation in the volume layers. The highest correlation occurs around 30 m above ground level, aligning with previous research in developing algorithms for the BIOMASS mission in aboveground biomass (AGB) retrieval. Together, TomoSAR and GEDI are robust and comparable in studying tropical forests and support the BIOMASS mission for global biomass mapping. [ABSTRACT FROM AUTHOR]

This paper presents a method to characterize snow cover in mountainous regions using dual-polarization C-band synthetic aperture radar (SAR) data. It is demonstrated that an accurate modeling of the liquid water distribution inside the snowpack, using a multilayer meteorological snow model, is required to characterize snow with precision. A multilayer-snow electromagnetic (EM) backscattering model is developed based on the vector radiative transfer, the strong fluctuation theory, and physical parameters supplied by the meteorological model. However, the limited resolution of the meteorological snow model is insufficient for predicting a refined EM backscattering at a massif scale. An adequate spatial reorganization of these snow profiles, based on a comparison between simulated and measured dual-polarization SAR data, leads to a better estimation of some snowpack parameters. In particular, the monitoring of snow liquid water content is presented improving the capacity of wet snow mapping as compared to a classical SAR-based method. This methodology shows good capacities both for qualitative and quantitative snow assessments, opening the way for a new operational method. Index Terms--Remote sensing, snow, synthetic aperture radar (SAR).

This paper deals with spatial analysis of texture in synthetic aperture radar (SAR) images. A new parametric model for local two-point statistics of the image is introduced, in order to characterize the spatially nonstationary and anisotropic behavior of the image. The texture is first modeled by a nonstationary Gaussian process resulting from the convolution of a Gaussian white noise with a field of anisotropic Gaussian kernel with spatially varying parameters. Hence, under the hypothesis of locally stationary signal, the analytic expression of the local autocovariance is derived. It is then explained how to simulate nonstationary K-distributed random fields by combining the new model with an already existing simulation method. A method for parameter estimation is then introduced. This method, based on the statistical product model, first corrects the speckle contribution to the local autocovariance and estimates the parameters of the model by analyzing the shape of the autocovariance. The algorithm is then evaluated over simulated and experimental data. Stationary simulations permit to show that, for a sufficient sample size, the estimator is unbiased. A test over a nonstationary simulation proves the ability of the algorithm to capture the spatial fluctuations of the texture. Finally, the method is applied to the experimental SAR data, and it is shown that a large amount of spatial information may be retrieved from the data. Index Terms--Autocovariance modeling, feature extraction, image texture analysis, nonstationary random process, synthetic aperture radar (SAR).

Fully polarimetrie synthetic aperture radar (SAR) data analysis has found wide application for terrain classification, land-use, soil moisture, and ground cover classification. Critical to all analyses and applications is accurate calibration of the relative amplitudes of and phases between the various polarimetric channels. Here we propose an a posteriori method imposing only the weakest of constraints, scattering reciprocity, on the polarimetric data. Calibration parameters are self-consistently estimated from full 4 x 4 polarimetric covariance matrices. Whilst the complete set of calibration parameters is underdetermined, we give several reasonable heuristic methods to provide a complete calibration. Stronger constraints reduce the number of independent parameters and provide an overdetermined set of equations but at a cost--the loss of polarimetric fidelity when the underlying assumptions are violated. Without recourse to in situ calibration targets, the extent of the polarimetric distortion that results from polarimetric calibration remains unknown. We apply our new method to simulated data, anechoic chamber data and polarimetric SAR imagery. We also present comparisons with alternate calibration methods and different approximate solutions of the new technique. Index Terms--Calibration, radar polarimetry.

A new concept in polarimetric synthetic aperture radar (POLSAR) speckle filtering that preserves the dominant scattering mechanism of each pixel is proposed in this paper. The basic principle is to select pixels of the same scattering characteristics to be included in the filtering process. To achieve this, the algorithm first applies the Freeman and Durden decomposition to separate pixels into three dominant scattering categories: surface, double bounce, and volume, and then unsupervised classification is applied. Speckle filtering is performed using the classification map as a mask. A single-look or multilook pixel centered in a 9 x 9 window is filtered by including only pixels in the same and two neighboring classes from the same scattering category. This filter is effective in speckle reduction, while perfectly preserving strong point target signatures, and retains edges, linear, and curved features in thePOLSAR data. The effect of speckle filtering on scattering characteristics, such as entropy, anisotropy, and alpha angle, will be discussed. Index Terms--Polarimetric synthetic aperture radar (POLSAR), radar polarimetry, speckle filtering.

In this paper, we proposed an unsupervised terrain and land-use classification algorithm using polarimetric synthetic aperture radar data. Unlike other algorithms that classify pixels statistically and ignore their scattering characteristics, this algorithm not only uses a statistical classifier, but also preserves the purity of dominant polarimetric scattering properties. This algorithm uses a combination of a scattering model-based decomposition developed by Freeman and Durden and the maximum-likelihood classifier based on the complex Wishart distribution. The first step is to apply the Freeman and Durden decomposition to divide pixels into three scattering categories: surface scattering, volume scattering, and double-bounce scattering. To preserve the purity of scattering characteristics, pixels in a scattering category are restricted to be classified with other pixels in the same scattering category. An efficient and effective class initialization scheme is also devised to initially merge clusters from many small clusters in each scattering category by applying a merge criterion developed based on the Wishart distance measure. Then, the iterative Wishart classifier is applied. The stability in convergence is much superior to that of the previous algorithm using the entropy/anisotropy/Wishart classifier. Finally, an automated color rendering scheme is proposed, based on the classes' scattering category to code the pixels to resemble their natural color. This algorithm is also flexible and computationally efficient. The effectiveness of this algorithm is demonstrated using the Jet Propulsion Laboratory's AIRSAR and the German Aerospace Center's (DLR) E-SAR L-band polarimetric synthetic aperture radar images. Index Terms--Radar polarimetry, synthetic aperture radar (SAR), terrain classification.

In synthetic aperture radar (SAR) polarimetry, the measured polarimetric signatures are used to analyze physical scattering properties of the imaged media. It is generally assumed that the sensor has a fixed orientation with respect to the objects. However, SAR sensors operating at lower frequencies, like L- and P-band, have a wide azimuth beamwidth, i.e., during the formation of the synthetic aperture, multiple squint angles are integrated to build the full-resolution SAR image. Variations in the polarimetric properties with the azimuthal look angle remain unconsidered. In this paper, a fully polarimetric subaperture analysis method is introduced. Using deconvolution, synthesized SAR images are decomposed into subaperture datasets, which correspond to the scene responses under different azimuthal look angles. A statistical analysis of the polarimetric parameters permits to clearly discriminate media showing a nonstationary behavior during the SAR integration. Finally, a method is proposed, which eliminates the influence of azimuthal backscattering variations in conventional polarimetric SAR data analysis. The effectiveness of the new methods is demonstrated on fully polarimetric SAR data, acquired by the German Aerospace Center (DLR) airborne experimental SAR sensor (E-SAR) at L-band. Index Terms--SAR polarimetry, subaperture processing, Bragg-resonance.

In this paper, we introduce a new classification scheme for dual frequency polarimetric SAR data sets. A (6 x 6) polarimetric coherency matrix is defined to simultaneously take into account the full polarimetric information from both images. This matrix is composed of the two coherency matrices and their cross-correlation. A decomposition theorem is applied to both images to obtain 64 initial clusters based on their scattering characteristics. The data sets are then classified by an iterative algorithm based on a complex Wishart density function of the 6 x 6 matrix. A class number reduction technique is then applied on the 64 resulting clusters to improve the efficiency of the interpretation and representation of each class. An alternative technique is also proposed which introduces the polarimetric cross-correlation information to refine the results of classification to a small number of clusters using the conditional probability of the cross-correlation matrix. These classification schemes are applied to full polarimetric P, L, and C-band SAR images of the Nezer Forest, France, acquired by the NASA/JPL AIRSAR Sensor in 1989. Index Terms--Multivariate statistics, radar polarimetry, synthetic aperture radar (SAR), terrain classification.