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For the quantitative applications of the Suomi National Polar-orbiting Partnership (SNPP) Advanced Technology Microwave Sounder (ATMS), the geolocation accuracy of its sensor data records must be quantified during its on-orbit operation. In this paper, a refined coastline inflection point method is used to evaluate the on-orbit geolocation accuracy of SNPP ATMS. It is disclosed that for SNPP ATMS, the static error term with scan-angle-dependent feature is a dominant part among all the geolocation error sources. A mathematical model is then developed to convert the in-track and cross-track geolocation errors to the beam pointing Euler angles defined in the spacecraft coordinate system, which can be further used to construct the correction matrix for on-orbit geolocation process. By using the correction matrix built in this paper, the geolocation error is obviously reduced both at nadir and at the edge of the scan. The total geolocation error at nadir before/after correction is 3.8/0.8 km at K-band, 5.6/0.8 km at Ka-band, 3.3/0.4 km at V-band, and 1.5/0.1 km at W-band. The geolocation bias at the edge of the scan line before/after correction is 4.6/1.3 km at K-band, 9.4/1.8 km at Ka-band, 4.4/2.4 km at V-band, and 3.2/0.8 km at W-band. After correction, the scan-angle-dependent feature in geolocation error is also largely reduced. [ABSTRACT FROM AUTHOR]

This paper deals with signal reconstruction for multichannel high-resolution wide-swath synthetic aperture radar (HRWS-SAR) systems. One of the commonly used algorithms for processing HRWS-SAR data is Krieger's digital beamforming (DBF) algorithm. This algorithm works quite well for cases with uniform space–time sampling as well as moderately nonuniform sampling. However, for cases with highly nonuniform sampling, Krieger's DBF algorithm in its original form suffers from a severe degradation in the signal-to-noise ratio, and for cases with coinciding sampling, this algorithm even fails to function. In this paper, we will solve these problems such that an improved DBF (IDBF) algorithm is developed. The key of the IDBF algorithm consists of two procedures: The first one implements an operation called ambiguity-index screening, which is based on two novel quantities, termed the equivalent sampling spacing and the effective reconstructed Doppler bandwidth; and the other key procedure implements a Doppler-spectrum weighting based on a newly defined parameter that is termed the maximum-to-minimum ratio of the noise spectrum. Simulation results show that the newly proposed IDBF algorithm shares a pronounced improvement in the imaging performance over Krieger's DBF algorithm. Moreover, simulation results also demonstrate that the IDBF algorithm exhibits a more robust and consistent overall performance than several other existing reconstruction algorithms. [ABSTRACT FROM PUBLISHER]

Establishing causal relations between random variables from observational data is perhaps the most important challenge in today’s science. In remote sensing and geosciences, this is of special relevance to better understand the earth’s system and the complex interactions between the governing processes. In this paper, we focus on an observational causal inference, and thus, we try to estimate the correct direction of causation using a finite set of empirical data. In addition, we focus on the more complex bivariate scenario that requires strong assumptions and no conditional independence tests can be used. In particular, we explore the framework of (nondeterministic) additive noise models, which relies on the principle of independence between the cause and the generating mechanism. A practical algorithmic instantiation of such principle only requires: 1) two regression models in the forward and backward directions and 2) the estimation of statistical independence between the obtained residuals and the observations. The direction leading to more independent residuals is decided to be the cause. We instead propose a criterion that uses the sensitivity (derivative) of the dependence estimator, the sensitivity criterion allows to identify samples most affecting the dependence measure, and hence, the criterion is robust to spurious detections. We illustrate the performance in a collection of 28 geoscience causal inference problems, a database of radiative transfer models simulations and machine learning emulators in vegetation parameter modeling involving 182 problems, and assessing the impact of different regression models in a carbon cycle problem. The criterion achieves the state-of-the-art detection rates in all cases, and it is generally robust to noise sources and distortions. The presented approach confirms the validity in observational bivariate problems in the earth sciences. [ABSTRACT FROM AUTHOR]

This paper departs from the popular usage of the Backus-Gilbert inversion (BGI) method as a tool for inversion of antenna temperature measurements in microwave radiometry. The BGI method is applied in this paper to enhance the information content of an existing set of oversampled brightness-temperature (TB) data. The purpose is to isolate the inversion process from its resolution enhancement counterpart. The advantage gained is that the resolution enhancement can be performed in a simplified way and in a different level of processing that starts with the scan-mode TB data product and simply requires with it the knowledge of the antenna gain pattern and the sensor's scan geometry. The technique is demonstrated with the 19.35-GHz Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) channel, which provides oversampled TB data. The radiometric resemblance of this channel with that of the 37 GHz and geocollocation of their TB footprints facilitate validation of the enhancement of features. The significance of oversampling the low-frequency (LF) radiometer channels is underscored in the process, which gives the authors the confidence to propose oversampling of the LF data for the forthcoming sensor Microwave Analysis and Detection of Rain and Atmospheric Structures (MADRAS) onboard the Megha-Tropiques mission, which is a joint ISRO-CNES collaboration (due for launch in 2009). [ABSTRACT FROM AUTHOR]

Hyperspectral images exhibit strong dependencies across spatial and spectral neighbors, which have been proved to be very useful for hyperspectral image classification. The recently defined conditional random field (CRF) can effectively model and use the dependencies for classification of hyperspectral images in a unified probabilistic framework. However, in order to be computationally tractable, the usual CRFs are limited to incorporate only pairwise potentials. Thus, the usual CRFs can capture only pairwise interactions and neglect higher order dependencies, which are potentially useful high-level properties particularly for the classification of hyperspectral image consisting of complex components. This paper overcomes this limitation by developing hyperspectral image classification algorithm based on a CRF with sparse higher order potentials, which are specially designed to incorporate complex characteristics of hyperspectral images. To efficiently implement the CRF model at training step, this paper develops an efficient local method under the piecewise training framework, while at inference step, this proposes a simple strategy to combine the piecewisely trained model to overcome the possible over-counting problems. Moreover, the combined model with the specially defined potentials can be efficiently inferred by graph cut method. Experiments on the real-world data attest to the accuracy, effectiveness, and efficiency of the proposed model on modeling and classifying hyperspectral images. [ABSTRACT FROM AUTHOR]

This paper investigates RGB color composition schemes for hyperspectral imagery display. A three-channel composite inevitably loses a significant amount of information contained in the original high-dimensional data. The objective here is to display the useful information as distinctively as possible for high-class separability. To achieve this objective, it is important to find an effective data processing step prior to color display. A series of supervised and unsupervised data transformation and classification algorithms are reviewed, implemented, and compared for this purpose. The resulting color displays are evaluated in terms of class separability using a statistical detector and perceptual color distance. We demonstrate that the use of the data processing step can significantly improve the quality of color display, whereas data classification generally outperforms data transformation, although the implementation is more complicated. Several instructive suggestions for practitioners are provided. [ABSTRACT FROM AUTHOR]

The frequency scaling algorithm (FSA) was proposed to process the synthetic aperture radar (SAR) data acquired via the dechirp-on-receive approach. Some aspects of improving the FSA are investigated in this paper, based on which an extended FSA (EFSA) is presented. The general purpose of the EFSA is to reduce the effect of range spectrum shift of the intermediate processing results, which occurs during the scaling operation in the FSA, so as to achieve a more effective utilization of the processed bandwidth. The EFSA is implemented through time shifting the scaling and the inverse scaling functions used in the FSA and also the adjustment of the scaling factor. The derivation of the EFSA is detailed in this paper. Point target simulation in squinted imaging geometry indicates that the presented algorithm is more suitable for large-squint applications. [ABSTRACT FROM AUTHOR]

In this paper, we address the theoretical aspects of coherence region modeling and its inversion methods for polarimetric interferometric synthetic aperture radar (PolInSAR). Instead of only considering the geometrical shape of the coherence region, we focus on directly modeling the whitened interferometric cross-correlation matrix of the PolInSAR data. In particular, we consider three classes of generic scattering models that respectively contain single, double, and triple phase centers within one resolution cell. We then demonstrate, for each class, that the modeled whitened interferometric cross-correlation matrix is normal. Based on this property, we propose, for each case, efficient algorithms to obtain the best normal matrix approximation solution for model inversion from the observed whitened interferometric cross-correlation matrix. In addition, we show how a simple and effective criterion can be designed from the derived solutions for model validation. Finally, we verify the solutions with both simulated and real data. The results prove the proposed method to be a very promising tool for PolInSAR applications. [ABSTRACT FROM PUBLISHER]

A linear frequency-modulated continuous-wave (LFMCW) synthetic aperture radar (SAR) is a highly miniaturized SAR that is suitable for use on a small unmanned aerial vehicle. In squint stripmap mode, the LFMCW SAR image quality is degraded by the presence of nonlinearities in the transmitted signal, combined with the large bandwidth introduced by the frequency scaling in the nonlinear frequency scaling (NLFS) algorithm. This paper proposes an extension of the NLFS algorithm called the Doppler-centroid-dependent frequency scaling algorithm. The algorithm introduces a new Doppler-centroid-dependent scaling factor to decrease the introduced bandwidth, effectively removing the range frequency aliasing, without extra computational load. Nonlinearity correction is performed at the same time as range cell migration correction. The validity of the proposed algorithm is demonstrated with point target simulation results. [ABSTRACT FROM AUTHOR]

This paper addresses the remote sensing image pan-sharpening problem from the perspective of compressed sensing (CS) theory which ensures that with the sparsity regularization, a compressible signal can be correctly recovered from the global linear sampled data. First, the degradation model from a high- to low-resolution multispectral (MS) image and high-resolution panchromatic (PAN) image is constructed as a linear sampling process which is formulated as a matrix. Then, the model matrix is considered as the measurement matrix in CS, so pan-sharpening is converted into signal restoration problem with sparsity regularization. Finally, the basis pursuit (BP) algorithm is used to resolve the restoration problem, which can recover the high-resolution MS image effectively. The QuickBird and IKONOS satellite images are used to test the proposed method. The experimental results show that the proposed method can well preserve spectral and spatial details of the source images. The pan-sharpened high-resolution MS image by the proposed method is competitive or even superior to those images fused by other well-known methods. [ABSTRACT FROM AUTHOR]

In this paper, heterogeneous clutter models are used to describe polarimetric synthetic aperture radar (PolSAR) data. The KummerU distribution is introduced to model the PolSAR clutter. Then, a detailed analysis is carried out to evaluate the potential of this new multivariate distribution. It is implemented in a hierarchical maximum likelihood segmentation algorithm. The segmentation results are shown on both synthetic and high-resolution PolSAR data at the X- and L-bands. Finally, some methods are examined to determine automatically the “optimal” number of segments in the final partition. [ABSTRACT FROM AUTHOR]

In this paper, we propose a Bayesian methodology for receiver function analysis, a key tool in determining the deep structure of the Earth's crust. We exploit the assumption of sparsity for receiver functions to develop a Bayesian deconvolution method as an alternative to the widely used iterative deconvolution. We model samples of a sparse signal as i.i.d. Student-t random variables. Gibbs sampling and variational Bayes techniques are investigated for our specific posterior inference problem. We used those techniques within the expectation-maximization (EM) algorithm to estimate our unknown model parameters. The superiority of the Bayesian deconvolution is demonstrated by the experiments on both simulated and real earthquake data. [ABSTRACT FROM AUTHOR]

The linear mixture model is a convenient way to describe image pixels as a linear combination of pure spectra – termed endmembers. The fractional contribution from each endmember is calculated through inversion of the linear model. Despite the simplicity of the model, a nonnegativity constraint that is imposed on the fractions leads to an unmixing problem for which it is hard to find a closed analytical solution. Current solutions to this problem involve iterative algorithms, which are computationally intensive and not appropriate for unmixing large number of pixels. This paper presents an algorithm to build fuzzy membership functions that are equivalent to the least square solution of the fully constrained linear spectral unmixing problem. The efficiency and effectiveness of the proposed solution is demonstrated using both simulated and real data. [ABSTRACT FROM AUTHOR]

The Cloud Profiling Radar, the sole science instrument of the CloudSat Mission, is a 94-GHz nadir-looking radar that measures the power backscattered by hydrometeors (clouds and precipitation) as a function of distance from the radar. This instrument has been acquiring global time series of vertical cloud structures since June 2, 2006. In this paper, an overview of the radar performance and status, to date, is provided together with a description of the basic data products and the surface clutter rejection algorithm introduced for the Release 04 data product. [ABSTRACT FROM AUTHOR]

Ultrawideband pulse radars have a great potential for high-range resolution in near field imaging and can be used for noncontact measuring in precision or specular products such as reflector antennas and aircraft fuselages. We have already proposed a high-speed 3-D imaging algorithm, SEABED, which is based on a reversible transform, which is the boundary scattering transform, between the received signals and the target shape. However, the estimated image with SEABED is unstable with random noise because it utilizes a derivative of the received data. In this paper, we propose a robust 3-D imaging algorithm with an envelope of spheres that completely resolves the instability due to derivative operations. Moreover, to enhance the resolution of estimated images, this method is combined with a direct waveform compensation method that does not sacrifice high-speed calculation. Numerical simulations and an experiment confirm that the proposed method can realize fast, robust, and high-resolution 3-D imaging for arbitrary targets. [ABSTRACT FROM AUTHOR]

In this paper, we present a new variant of the k-nearest neighbor (kNN) classifier based on the maximal margin principle. The proposed method relies on classifying a given unlabeled sample by first finding its k-nearest training samples. A local partition of the input feature space is then carried out by means of local support vector machine (SVM) decision boundaries determined after training a multiclass SVM classifier on the considered k training samples. The labeling of the unknown sample is done by looking at the local decision region to which it belongs. The method is characterized by resulting global decision boundaries of the piecewise linear type. However, the entire process can be kernelized through the determination of the k-nearest training samples in the transformed feature space by using a distance function simply reformulated on the basis of the adopted kernel. To illustrate the performance of the proposed method, an experimental analysis on three different remote sensing datasets is reported and discussed. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid reconstruction algorithm (HRA) is presented to solve the ill-posed inverse problem associated with 3-D ionospheric stochastic tomography. In this new method, the ionospheric electron density (IED) can be inverted by using two steps. First, a truncated singular value decomposition (TSVD) method, whose value is independent on any initial estimation, is used to resolve the ill-posed problem of the tomography system. Second, taking into account the "approximation" of its solution, an iterative improvement process of the solution is then implemented by utilizing the conventional algebraic reconstruction algorithm (ART). The HRA, therefore, offers a more reasonable approach to choose an initial approximate for the ART and to improve the quality of the final reconstructed image. A simulated experiment demonstrates that the HRA method is superior to the TSVD or the ART alone for the tomographic inversion of LED. Finally, the HRA is used to perform GPS-based tomographic reconstruction of the IED at mid- and low-latitude regions. [ABSTRACT FROM AUTHOR]

It is important to monitor environmental changes of the Earth's cover by remotely sensed data. This paper analyzes seasonal changes of a wetland by a modified polarimetric four-component scattering power decomposition method. The data sets analyzed here are L- and X-band fully polarimetric synthetic aperture radar (POLSAR) data, which have been acquired by the NICT/JAXA airborne polarimetric and interferometric synthetic aperture radar system in 2004. Since there existed a deficiency in the currently adopted decomposition schemes in that negative powers appear in a few pixels in the image analysis, we modified the approach taking into account physical conditions. It is shown by the modified scheme that the seasonal changes and features of the vegetation near Sakata Lagoon in Niigata, Japan, are observed clearly, demonstrating the utility of POLSAR image analysis for wetland assessments in general. [ABSTRACT FROM AUTHOR]

We propose a classification method suitable for high-resolution synthetic aperture radar (SAR) images over urban areas. When processing SAR images, there is a strong need for statistical models of scattering to take into account multiplicative noise and high dynamics. For instance, the classification process needs to be based on the use of statistics. Our main contribution is the choice of an accurate model for high-resolution SAR images Over urban areas and its use in a Markovian classification algorithm. Clutter in SAR images becomes non-Gaussian when the resolution is high or when the area is man-made. Many models have been proposed to fit with non-Gaussian scattering statistics (κ, Weibull, Log-normal, Nakaganhi-Rice, etc.), but none of them is flexible enough to model all kinds of surfaces in our context. As a consequence, we use a mathematical model that relies on the Fisher distribution and the log-moment estimation and which is relevant for one-look data. This estimation method is based on the second-kind statistics, which are detailed in the paper. We also prove its accuracy for urban areas at high resolutions The quality of the classification that is obtained by mixing this model and a Markovian segmentation is high and enables us to distinguish between ground; buildings, and vegetation. [ABSTRACT FROM AUTHOR]

Describes the use of the multiple signal classification (MUSIC) algorithm in three-dimensional (3-D) estimation target positions in ground-penetrating radar (GPR). Factors that limit bandwidth; Signal processing method for estimating 3-D positions using the conformal array; Examples of target positioning with both computer simulation and experiments.

At the core of the technical methodology for creating the Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER) global digital elevation model (GDEM) is the procedure for generating a global set of 1 ^\circ latitude-by-1^ \circ longitude tiles containing DEM data in geographic latitude and longitude coordinates and with one arc second postings from scene-based ASTER DEMs. The ASTER GDEM is comprised of all tiles, which include at least 0.01% land in them, each containing 3601-by-3601 elevation data points. The tiles are created by stacking all observed scene DEM data matched geographically to the tile container, selecting valid data for each pixel, removing abnormal data values, and then averaging the remaining selected valid data to assign as the tile elevation data. Valid Earth surface elevation values typically clump within a \pm40-m range and are assumed to be lower in elevation than residual cloud outliers. The filtering process, which assigns the tile elevation data, is one of the most important parts of the GDEM generation system. The median-based selection method is designed to efficiently select the valid data for each pixel. The combination of cloud-masked and non-cloud-masked data is another important part of the process to assign accurate elevation data for each pixel, because the cloud masking capability is not perfect. The algorithm used to combine both data is described. The postprocessing for inland water bodies is successfully carried out to yield a flattened elevation value. This postprocessing is essential to assign unique elevation values for each inland water body. GDEM tile elevation data include some residual anomalies, mostly in areas with fewer than three valid stacked input scenes. The correction method using existing reference data also is described. [ABSTRACT FROM PUBLISHER]

Consistent, continuous, and long time series of global biophysical variables derived from satellite data are required for global change research. A novel climatology fitting approach called CACAO (Consistent Adjustment of the Climatology to Actual Observations) is proposed to reduce noise and fill gaps in time series by scaling and shifting the seasonal climatological patterns to the actual observations. The shift and scale CACAO parameters adjusted for each season allow quantifying shifts in the timing of seasonal phenology and inter-annual variations in magnitude as compared to the average climatology. CACAO was assessed first over simulated daily Leaf Area Index (LAI) time series with varying fractions of missing data and noise. Then, performances were analyzed over actual satellite LAI products derived from AVHRR Long-Term Data Record for the 1981–2000 period over the BELMANIP2 globally representative sample of sites. Comparison with two widely used temporal filtering methods—the asymmetric Gaussian (AG) model and the Savitzky-Golay (SG) filter as implemented in TIMESAT—revealed that CACAO achieved better performances for smoothing AVHRR time series characterized by high level of noise and frequent missing observations. The resulting smoothed time series captures well the vegetation dynamics and shows no gaps as compared to the 50–60\% of still missing data after AG or SG reconstructions. Results of simulation experiments as well as confrontation with actual AVHRR time series indicate that the proposed CACAO method is more robust to noise and missing data than AG and SG methods for phenology extraction. [ABSTRACT FROM PUBLISHER]

A contrast-based phase calibration algorithm for digital beamforming remote sensing radars using three contrast metrics is presented. The algorithm corrects time-varying antenna array phase errors that defocus digital beamforming remote sensing radar imagery. Amplitude errors are treated by equalizing the received powers in all elements. As such, the algorithm does not produce an absolute (or radiometric) calibration vector for the array. The performance of the algorithm is studied using a combination of simulated and real radar data under various conditions and is compared with a clutter-based calibration algorithm. An analytical proof showing that maximizing the expected value of the 4-norm metric is equivalent to phase-calibrating the image, except for a linear phase offset, is provided. We find that the clutter calibration algorithm performs best for statistically homogeneous scenes but that the contrast-calibration algorithms perform better with scenes with larger contrast ratios. [ABSTRACT FROM PUBLISHER]

Total backscatter-coefficient inversion error bounds for the two-component lidar inversion algorithm (so-called Fernald's or Klett-Fernald-Sasano's method) are derived in analytical form in response to the following three error sources: 1) the measurement noise; 2) the user uncertainty in the backscatter-coefficient calibration; and 3) the aerosol extinction-to-backscatter ratio. The following two different types of error bounds are presented: 1) approximate error bounds using first-order error propagation and 2) exact error bounds using a total-increment method. Both error bounds are formulated in explicit analytical form, which is of advantage for practical physical sensitivity analysis and computational implementation. A Monte Carlo approach is used to validate the error bounds at 355-, 532-, and 1064-nm wavelengths. [ABSTRACT FROM AUTHOR]

A new ray-tracing method in 3-D heterogeneous isotropic media is proposed based on the bilinear travel-time interpolation and wavefront group marching method (GMM). In this method, an irregular cell discretization scheme is used to accurately describe arbitrarily undulant interfaces in a model. The ray tracing is carried out by forward–backward processing. In the forward step, the travel time in an irregular cell is expressed in terms of the bilinear interpolation of the known travel times on the cell's surfaces. Then, the wavefront is evolved from the source to the whole computational domain by using the newly developed travel-time solver and the fast wavefront expansion GMM. In the backward step, each ray path is traced from the receiver by finding the intersection points of potential ray propagation vectors with the surfaces of the relevant cells. The same travel-time solver is used to compute the candidate intersection points on all surfaces of each relevant cell, and the point with the minimum travel time is selected as a ray point from which the similar step is continued until the sources are found. Several numerical experiments are presented to demonstrate that the new algorithm is accurate, efficient, and robust. [ABSTRACT FROM AUTHOR]

Microphysical and dynamical features of volcanic ash clouds can be quantitatively monitored by using ground-based microwave weather radars. These systems can provide data for determining the ash volume, total mass, and height of eruption clouds. In order to demonstrate the unique potential of this microwave active remote-sensing technique, the case study of the eruption of Augustine Volcano in Alaska in January 2006 is described and analyzed. Volume scan data, acquired by a NEXRAD WSR-88D S-band ground-based weather radar, are processed to automatically classify and estimate eruptive cloud particle concentration. The numerical results of the coupled model Z-reflectivity from Active Tracer High resolution Atmospheric Model (ATHAM), including particle aggregation processes and simulation of radar reflectivity from the ATHAM microphysical model, are exploited to train the inversion algorithm. The volcanic ash radar retrieval based on the ATHAM algorithm is a physical-statistical approach based on the backscattering microphysical model of volcanic cloud particles (hydrometeors, ash, and aggregates), used within a Bayesian classification and optimal regression algorithm. A sensitivity analysis is carried out to evaluate the overall error budget. The evolution of the Augustine eruption is discussed in terms of radar measurements and products, pointing out the unique features, the current limitations, and future improvements of radar remote sensing of volcanic plumes. [ABSTRACT FROM AUTHOR]

Multiobjective segmentation algorithms are based on an objective function, consisting of two or more terms, that is minimized by using an optimization algorithm. The objective terms represent differing segmentation objectives, the most popular of which are statistical likelihood of pixel values and smoothness of segment boundaries. Many assumptions are built into the objective function, and we present a case study based on the algorithm of Stewart et al. to demonstrate the importance of analyzing algorithm characteristics to test the validity of hidden assumptions. We develop a set of simulated test images and a novel segmentation performance metric for use with simulated data. An innovative aspect of the Stewart algorithm (SA) is the probability of false alarm (PFA) model used to weight the objective terms. This is intended to dynamically balance the terms as the algorithm progresses. The PFA model is only valid for false edges, and we have shown that the number of selected true edges increases as segmentation evolves, making the theoretical weight model increasingly invalid. In addition, we found problems with several other algorithm assumptions. We tested algorithm performance against a fixed-weight version. We found that the performance of the SA was worse than a fixed-weight version. Thus, while the two-term objective function algorithm does deliver reasonable performance for multilook data, the fixed-weight version gives better performance. While these results hold only for simulated data, we believe that the experimental results indicate the need for a more powerful approach to multiobjective synthetic aperture radar segmentation. [ABSTRACT FROM AUTHOR]

Part I. Presents a study which described an operational non-linear physical inversion (PIn) algorithm for precipitable and cloud liquid water estimate. Standard atmospheric temperature profile of the forward model; Validation of the forward model; Parameters retrieval for a fixed tropopause temperature.

Deals with a study that evaluated the accuracy of the digital elevation model generated by the Jet Propulsion Laboratory TOPSAR for extracting canopy height. Description of the test site and the ground-based measurements; Analysis of the two height estimation algorithms; Conclusions.

Discusses a study which presented a numerical algorithm based on the radiative transfer equation (RTE) which allows the calculation of the scattering and penetration of isolated vegetation specimens. Details on the RTE in vegetation; Discretization of the RTE; Information on the iterative approach for the discretized RTE (diRTE); Parameters of the diRTE algorithm; Conclusions.

Discusses reconstruction capabilities and solution strategies of multifrequency information in inverse scattering problems. Problems with multifrequency information; Advantages of frequency diversity and choice of frequencies; Solution algorithms and their performances.

Describes the implementation of electric-field, magnetic-field, and combined-field integral equations in extension of the multilevel fast multipole algorithm (MLFMA) for electromagnetic scattering. Discussion on the MLFMA formalism for surface or buried perfectly conducting (PEC) targets; Conclusion.