Geoscience and Remote Sensing, IEEE Transactions on - new TOC

Front cover

Feb. 2012

IEEE Transactions on Geoscience and Remote Sensing publication information

Feb. 2012

Introduction to Special Section on Space Technology

Feb. 2012

The eight papers in this special section focus on space technology.

TGRS Space Technology Special Section List of Reviewers

Feb. 2012

Near-Space Vehicle-Borne SAR With Reflector Antenna for High-Resolution and Wide-Swath Remote Sensing

Feb. 2012

Near-space is recognized as the atmospheric region from 20 to 100 km above the Earth's surface. Near-space vehicles offer several advantages compared to low earth orbit satellites and airplanes because near-space vehicles are not constrained by orbital mechanics and fuel consumption. These advantages provide potential for future remote sensing applications, but little related work has been published. This paper explains what near-space is and how it should be exploited for remote sensing applications. A near-space vehicle-borne synthetic aperture radar (SAR) with reflector antenna and digital beamforming on receive is proposed for high-resolution and wide-swath (HRWS) remote sensing. The system configuration, signal model, imaging scheme, system performance, and nadir echo suppression are investigated. An example system is conceptually designed, along with its system performance analysis. It is shown that the near-space vehicle-borne SAR with reflector antenna can operate with high flexibility and reconfigurability, thus enabling a satisfactory HRWS remote sensing performance.

Improved Range Ambiguity Performance in Quad-Pol SAR

Feb. 2012

Conventional quadrature-polarimetric (quad-pol) synthetic aperture radar (SAR) systems operating from space are severely constrained by their limited range of useful incident angles and their reduced swath widths particularly at larger incidence. These limitations are due primarily to relatively severe range ambiguities in the cross-polarized measurement channels. The conventional approach for quad-pol SAR systems uses linear polarizations on both transmit and receive. Range ambiguities can be markedly reduced by adopting hybrid-polarimetric architecture. In this approach, the radar transmits circularly polarized waves but receives on orthogonal linear polarizations. The sense of the circular polarization-left or right-is reversed on alternate transmissions. Hybrid-polarimetric quad-pol architecture leads to hardware that is more readily calibrated because neither receive channel is cross polarized with respect to the transmitted polarization; hence, their mean signal levels are the same. The data provided by a hybrid-polarimetric quad-pol SAR may be transformed into the conventional linearly polarized scattering matrix, thus preserving compatibility with the rich heritage of analysis tools developed for such radars.

Geolocation of Argus Flight Data

Feb. 2012

In this paper, we briefly describe the Argus spectrometer and its mission. We then focus on the process to determine the geolocation of the spectrometer's flight data. For the Canadian Advanced Nanospace eXperiment 2 (CanX-2) Argus flight, we have used Simplified General Perturbations 4 (SGP4) propagation for position determination. Two sets of flight data are presented as examples. We estimate the uncertainty in the geolocation of Argus data using this method and investigate potential improvements for future Argus flights.

An FPGA-Based Hardware Implementation of Configurable Pixel-Level Color Image Fusion

Feb. 2012

Image fusion has attracted a lot of interest in recent years. As a result, different fusion methods have been proposed mainly in the fields of remote sensing and computer (e.g., night) vision, while hardware implementations have been also presented to tackle real-time processing in different application domains. In this paper, a linear pixel-level fusion method is employed and implemented on a field-programmable-gate-array-based hardware system that is suitable for remotely sensed data. Our work incorporates a fusion technique (called VTVA) that is a linear transformation based on the Cholesky decomposition of the covariance matrix of the source data. The circuit is composed of different modules, including covariance estimation, Cholesky decomposition, and transformation ones. The resulted compact hardware design can be characterized as a linear configurable implementation since the color properties of the final fused color can be selected by the user in a way of controlling the resulting correlation between color components.

FPGA Implementation of the N-FINDR Algorithm for Remotely Sensed Hyperspectral Image Analysis

Feb. 2012

Hyperspectral remote sensing attempts to identify features in the surface of the Earth using sensors that generally provide large amounts of data. The data are usually collected by a satellite or an airborne instrument and sent to a ground station that processes it. The main bottleneck of this approach is the (often reduced) bandwidth connection between the satellite and the station, which drastically limits the information that can be sent and processed in real time. A possible way to overcome this problem is to include onboard computing resources able to preprocess the data, reducing its size by orders of magnitude. Reconfigurable field-programmable gate arrays (FPGAs) are a promising platform that allows hardware/software codesign and the potential to provide powerful onboard computing capability and flexibility at the same time. Since FPGAs can implement custom hardware solutions, they can reach very high performance levels. Moreover, using run-time reconfiguration, the functionality of the FPGA can be updated at run time as many times as needed to perform different computations. Hence, the FPGA can be reused for several applications reducing the number of computing resources needed. One of the most popular and widely used techniques for analyzing hyperspectral data is linear spectral unmixing, which relies on the identification of pure spectral signatures via a so-called endmember extraction algorithm. In this paper, we present the first FPGA design for N-FINDR, a widely used endmember extraction algorithm in the literature. Our system includes a direct memory access module and implements a prefetching technique to hide the latency of the input/output communications. The proposed method has been implemented on a Virtex-4 XC4VFX60 FPGA (a model that is similar to radiation-hardened FPGAs certified for space operation) and tested using real hyperspectral data collected by NASA's Earth Observing-1 Hyperion (a satellite instrument) and the Airborne Visible Infra-- ed Imaging Spectrometer over the Cuprite mining district in Nevada and the Jasper Ridge Biological Preserve in California. Experimental results demonstrate that our hardware version of the N-FINDR algorithm can significantly outperform an equivalent software version and is able to provide accurate results in near real time, which makes our reconfigurable system appealing for onboard hyperspectral data processing.

A Saturated Light Correction Method for DMSP/OLS Nighttime Satellite Imagery

Feb. 2012

Several studies have clarified that electric power consumption can be estimated from the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) stable light imagery. As digital numbers (DNs) of stable light images are often saturated in the center of city areas, we developed a saturated light correction method for the DMSP/OLS stable light image using the nighttime radiance calibration image of the DMSP/OLS. The comparison between the nonsaturated part of the stable light image for 1999 and the radiance calibration image for 1996-1997 in major areas of Japan showed a strong linear correlation (R² = 92.73) between the DNs of both images. Saturated DNs of the stable light image could therefore be corrected based on the correlation equation between the two images. To evaluate the new saturated light correction method, a regression analysis is performed between statistic data of electric power consumption from lighting and the cumulative DNs of the stable light image before and after correcting for the saturation effects by the new method, in comparison to the conventional method, which is, the cubic regression equation method. The results show a stronger improvement in the determination coefficient with the new saturated light correction method (R² = 0.91, P = 1.7 ·10^-6 <; 0.05) than with the conventional method (R² = 0.81, P = 2.6 ·10^-6 <; 0.05) from the initial correlation with the uncorrected data (R² = 0.70, P = 4.5 · 10^-6 <; 0.05). The new method proves therefore to be very efficient for saturated light correction.

Markov Chain CFAR Detection for Polarimetric Data Using Data Fusion

Feb. 2012

This paper proposes a new Markov-chain-based constant false alarm rate (CFAR) detector for polarimetric data using low-level data fusion and high-level decision fusion. The Markov-chain-based CFAR detector extends traditional probability density function (pdf) based CFAR detection to first-order Markov chain model by considering both correlation between neighboring pixels and pdf information in CFAR detection. With the additional correlation information, the proposed approach results in advancing the performance of conventional CFAR detectors. Moreover, to take advantage of full polarizations of polarimetric data, various data fusion methods are considered to improve detection performance, including polarimetric transformation, principal component analysis, and decision fusion. Our experimental results confirm the superiority of the new Markov chain polarimetric CFAR detector over conventional pdf-based CFAR detectors.

Uncertainty Analysis of Neural-Network-Based Aerosol Retrieval

Feb. 2012

Neural networks have the ability to represent and learn complex regression functions and are very suitable for retrieval of geophysical parameters from remotely sensed data. Neural networks trained to minimize the mean square error are able to estimate the conditional expectation of target variables. In many remote sensing applications, it is also critical to provide estimates of prediction uncertainty. In this paper, we evaluate an approach that, in addition to training a neural network for retrievals, also trains a neural-network-based estimator of retrieval uncertainty. The uncertainty estimator is built under the assumption that uncertainty is a function of input variables. The methodology was evaluated on aerosol-optical-depth retrieval. The data set consists of 38 238 collocated Moderate Resolution Imaging Spectrometer (MODIS) satellite instrument and Aerosol Robotic Network ground-based instrument measurements collected over the entire Earth during two years (in 2005-2006). The results indicate that a neural network ensemble is more accurate than the operational MODIS retrieval algorithm called Collection 5 and that the retrieval uncertainty of the ensemble can be estimated with satisfactory accuracy.

Spectral Downscaling of Integrated Water Vapor Fields From Satellite Infrared Observations

Feb. 2012

Atmospheric water vapor is a crucial constituent affecting both climate change and hydrological cycle processes, whereas on the other hand, it has a significant impact on the electromagnetic signal propagation. Since the distribution of atmospheric water vapor strongly varies with time, location, and altitude, it is necessary to monitor it at high spatial and temporal resolution. Unfortunately, mapping its spatial distribution is difficult due to the lack of meteorological instrumentation at an adequate spatial and temporal observation scale. For many geophysical applications, there is also the need to reconstruct spatial details of integrated precipitable water vapor from information available only at coarser spatial scales. Spatial downscaling approaches can play a significant role when high-resolution water vapor retrievals from relatively new sensors, like synthetic aperture radars, or from conventional sensors, like the infrared radiometers MEdium Resolution Imaging Spectrometer (MERIS) or Moderate Resolution Imaging Spectroradiometer (MODIS), are used in synergy to enhance the accuracy of integrated water vapor retrievals. In this context, this paper introduces some new methodological aspects to increase the spatial resolution of integrated precipitable water vapor observations using a statistical downscaling spectral approach. To highlight the potential and the usefulness of the proposed downscaling estimation procedure, collocated 250-m MERIS and 1-km MODIS acquisitions are used. Results reveal the ability of spectral downscaling to reproduce quite well the second-order statistical variability of the water vapor field at small spatial scales with a root-mean-square error comparable with conventional interpolation techniques.

Evaluation and Wind Speed Dependence of MODIS Aerosol Retrievals Over Open Ocean

Feb. 2012

The Maritime Aerosol Network (MAN) data set provides high-quality ground truth to validate the Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol product over open ocean. Prior validation of the ocean aerosol product has been limited to coastal and island sites. Comparing MODIS Collection 5 ocean aerosol retrieval products with collocated MAN measurements from ships shows that MODIS is meeting the prelaunch uncertainty estimates for aerosol optical depth (AOD) with 64% and 67% of retrievals at 550 nm and 74% and 78% of retrievals at 870 nm, falling within expected uncertainty for Terra and Aqua, respectively. Angstrom exponent comparisons show a high correlation between MODIS retrievals and shipboard measurements (R = 0.85 for Terra and 0.83 for Aqua), although the MODIS aerosol algorithm tends to underestimate particle size for large particles and overestimate size for small particles, as seen in earlier collections. Prior analysis noted an offset between Terra and Aqua ocean AODs, without concluding which sensor was more accurate. The simple linear regression reported here is consistent with other anecdotal evidence that Aqua agreement with the Aerosol Robotic Network is marginally better. However, we cannot claim based on the current study that the better Aqua comparison is statistically significant. A systematic increase of error as a function of wind speed is noted in both Terra and Aqua retrievals. This wind speed dependence enters the retrieval when winds deviate from the 6-m/s value assumed in the rough ocean surface and white cap parameterizations. Wind speed dependence in the results can be mitigated by using auxiliary National Centers for Environmental Prediction wind speed information in the retrieval process.

Experimental Detection and Characterization of Brillouin Precursor Through Loamy Soil at Microwave Frequencies

Feb. 2012

This study reports experimental results on detecting Brillouin precursors through loamy-soil dispersive media in the frequency range of 500 MHz to 3 GHz. An experimental technique to collect and analyze the data is presented. Brillouin precursors are shown to be superimposed on the sine-modulated rectangular and Gaussian pulses. The detected Brillouin precursor is shown to have an algebraic amplitude decay behavior through the wet loamy soil considered in this study. Further, a method is proposed to extract the complex dielectric permittivity of the soil from limited experimental data. The experimental results are validated using a theoretical Fast Fourier Transform-based formulation and the experimentally achieved complex dielectric permittivity. These results are also compared with those of using existing theoretical dielectric models. Three different tests are also applied to validate the hypothesis of Brillouin precursor formation.

Improving Spatial Soil Moisture Representation Through Integration of AMSR-E and MODIS Products

Feb. 2012

The use of microwave observations has been highlighted as a complementary tool for evaluating land surface properties. Microwave observations are less affected by clouds, water vapor, and aerosol and also contain valuable soil moisture information. However, a critical limitation in microwave observations is the coarse spatial resolution attributed to the complex retrieval process. The objective of the current study is to develop an independent (from ground observations) downscaling approach that merges information from higher spatial resolution MODerate-resolution Imaging Spectroradiometer (MODIS) (~1 km) with lower spatial resolution AMSR-E (~25 km) to obtain soil moisture estimates at the MODIS scale (~1 km). We compare the developed (UCLA) method against a range of previous published approaches. Various key factors (i.e., surface temperature, vegetation indexes, and albedo) derived from MODIS provide information on relative variations in surface wetness conditions and contribute weighting parameters for downscaling the larger AMSR-E soil moisture footprints. Evaluation of the various downscaled soil moisture products is undertaken at the SMEX04 site in southern Arizona. Results show that the UCLA downscaling technique, as well as the previously published Merlin method, significantly improves the limited spatial variability of the current AMSR-E product. Spatial correlation (R) values improved from -0.08 to 0.34 and 0.27 for the Merlin and UCLA methods, respectively. The evaluated triangle-based methods show poorer performance over the study domain. Results from the current study yield insight on the integration of multiscale remote sensing data in various downscaling methods and the usefulness of MODIS observations in compensating for low-resolution microwave observations.

Application of QuikSCAT Backscatter to SMAP Validation Planning: Freeze/Thaw State Over ALECTRA Sites in Alaska From 2000 to 2007

Feb. 2012

The mapping of the predominant freeze/thaw state of the landscape is one of the main objectives of the National Aeronautics and Space Administration's proposed Soil Moisture Active Passive (SMAP) mission. This study applies Alaska Ecological Transect (ALECTRA) biophysical network temperature measurements and satellite radar scatterometer data from the Quick Scatterometer (QuikSCAT) to evaluate some of the validation issues regarding the planned SMAP freeze/thaw measurements. Although the QuikSCAT data are acquired at Ku-band frequency, rather than at the L-band frequency of the proposed SMAP instrument, QuikSCAT data do provide a high temporal fidelity over the ALECTRA sites, similar to SMAP. The results of this study show that multiple temperature measurements representative of individual landscape components (soil, snow cover, vegetation, and atmosphere) covering different types of terrain within the satellite field of view are important for understanding the freeze/thaw process and the aggregate radar backscatter response to that process. The backscatter temporal dynamics and relative contribution of the freeze/thaw state of these landscape elements to radar signal vary with land cover, seasonal weather, and climate conditions.

Comparison of Ray-Tracing Packages for Troposphere Delays

Feb. 2012

A comparison campaign to evaluate and compare troposphere delays from different ray-tracing software was carried out under the umbrella of the International Association of Geodesy Working Group 4.3.3 in the first half of 2010 with five institutions participating: the GFZ German Research Centre for Geosciences (GFZ), the Groupe de Recherche de Geodesie Spatiale, the National Institute of Information and Communications Technology (NICT), the University of New Brunswick, and the Institute of Geodesy and Geophysics of the Vienna University of Technology. High-resolution data from the operational analysis of the European Centre for Medium-Range Weather Forecasts (ECMWF) for stations Tsukuba (Japan) and Wettzell (Germany) were provided to the participants of the comparison campaign. The data consisted of geopotential differences with respect to mean sea level, temperature, and specific humidity, all at isobaric levels. Additionally, information about the geoid undulations was provided, and the participants computed the ray-traced total delays for 5^° elevation angle and every degree in azimuth. In general, we find good agreement between the ray-traced slant factors from the different solutions at 5^° elevation if determined from the same pressure level data of the ECMWF. Standard deviations and biases are at the 1-cm level (or significantly better for some combinations). Some of these discrepancies are due to differences in the algorithms and the interpolation approaches. If compared with slant factors determined from ECMWF native model level data, the biases can be significantly larger.

A Graphic Representation of the Necessary Condition for the MAFA Method

Feb. 2012

The modified ambiguity function approach (MAFA) is one of the integer least square estimation methods. Although it is a simple and efficient method, it has a significant limitation. This limitation is formulated and analyzed in this paper. A graphical representation of the necessary condition for the MAFA method is presented by plots of Voronoi cells and error ellipsoids, along with a detailed procedure for constructing these plots. It is shown that the necessary condition for the MAFA method is not satisfied in some cases. To overcome this problem, a solution is presented in the final section of this paper.

A Variable-Resolution Probabilistic Three-Dimensional Model for Change Detection

Feb. 2012

Given a set of high-resolution images of a scene, it is often desirable to predict the scene's appearance from viewpoints not present in the original data for purposes of change detection. When significant 3-D relief is present, a model of the scene geometry is necessary for accurate prediction to determine surface visibility relationships. In the absence of an a priori high-resolution model (such as those provided by LIDAR), scene geometry can be estimated from the imagery itself. These estimates, however, cannot, in general, be exact due to uncertainties and ambiguities present in image data. For this reason, probabilistic scene models and reconstruction algorithms are ideal due to their inherent ability to predict scene appearance while taking into account such uncertainties and ambiguities. Unfortunately, existing data structures used for probabilistic reconstruction do not scale well to large and complex scenes, primarily due to their dependence on large 3-D voxel arrays. The work presented in this paper generalizes previous probabilistic 3-D models in such a way that multiple orders of magnitude savings in storage are possible, making high-resolution change detection of large-scale scenes from high-resolution aerial and satellite imagery possible. Specifically, the inherent dependence on a discrete array of uniformly sized voxels is removed through the derivation of a probabilistic model which represents uncertain geometry as a density field, allowing implementations to efficiently sample the volume in a nonuniform fashion.

A Bicriteria-Optimization-Approach-Based Dimensionality-Reduction Model for the Color Display of Hyperspectral Images

Feb. 2012

This paper proposes a new nonlinear dimensionality-reduction model based on a bicriteria global optimization approach for the color display of hyperspectral images. The proposed fusion model is derived from two well-known and contradictory criteria of good visualization, which are useful in any multidimensional imagery color display, namely, accuracy, with the preservation of spectral distance criterion, and contrast, guaranteeing that colors are well distinguished or concretely allowing the good separability of each observed existing material in the final visualized color image. An internal parameter allows our algorithm to express the contribution or the importance of these two criteria for a specific application. In this framework, which also can be viewed as a classical Bayesian optimization strategy involving a tradeoff between fidelity to the unreduced (raw) spectral data and the expected highly contrasted resulting mapping, we will show that a hybrid optimization strategy, combining a global and deterministic optimization procedure and a local stochastic search using the Metropolis criterion, can be exploited to efficiently minimize the complex nonlinear objective cost function related to our model. The experiments reported in this paper demonstrate that the proposed model, taking into account these two criteria of good visualization, makes easier and more reliable the interpretation and quick overview of such multidimensional hyperspectral images.

A Simple and Robust Feature Point Matching Algorithm Based on Restricted Spatial Order Constraints for Aerial Image Registration

Feb. 2012

Accurate point matching is a critical and challenging process in feature-based image registration. In this paper, a simple and robust feature point matching algorithm, called Restricted Spatial Order Constraints (RSOC), is proposed to remove outliers for registering aerial images with monotonous backgrounds, similar patterns, low overlapping areas, and large affine transformation. In RSOC, both local structure and global information are considered. Based on adjacent spatial order, an affine invariant descriptor is defined, and point matching is formulated as an optimization problem. A graph matching method is used to solve it and yields two matched graphs with a minimum global transformation error. In order to eliminate dubious matches, a filtering strategy is designed. The strategy integrates two-way spatial order constraints and two decision criteria restrictions, i.e., the stability and accuracy of transformation error. Twenty-nine pairs of optical and Synthetic Aperture Radar (SAR) aerial images are utilized to evaluate the performance. Compared with RANdom SAmple Consensus (RANSAC), Graph Transformation Matching (GTM), and Spatial Order Constraints (SOC), RSOC obtained the highest precision and stability.

Coupled Nonnegative Matrix Factorization Unmixing for Hyperspectral and Multispectral Data Fusion

Feb. 2012

Coupled nonnegative matrix factorization (CNMF) unmixing is proposed for the fusion of low-spatial-resolution hyperspectral and high-spatial-resolution multispectral data to produce fused data with high spatial and spectral resolutions. Both hyperspectral and multispectral data are alternately unmixed into end member and abundance matrices by the CNMF algorithm based on a linear spectral mixture model. Sensor observation models that relate the two data are built into the initialization matrix of each NMF unmixing procedure. This algorithm is physically straightforward and easy to implement owing to its simple update rules. Simulations with various image data sets demonstrate that the CNMF algorithm can produce high-quality fused data both in terms of spatial and spectral domains, which contributes to the accurate identification and classification of materials observed at a high spatial resolution.

Ground Moving Target Signal Analysis in Complex Image Domain for Multichannel SAR

Feb. 2012

For along-track multichannel synthetic aperture radar, this paper proposes a novel ground moving target signal model in the high-resolution complex image domain. Based on the range-Doppler imaging of static scene, the 2-D complex response of an isolated rectilinearly moving target is derived via the stationary phase principle (SPP) approximations. It is shown that moving targets in complex domain can be divided into three types according to the 2-D motion distribution and the SPP approximation conditions. Different from the known peaklike response of a static target, different amplitude and phase modulations will appear for different types of moving targets. Moreover, a single target can be split into two targets in the image when its Doppler spectrum spreads over two ambiguous Doppler zones. All types of targets will have the same Doppler interferometric effect along multichannel images, which is decided by the target's ambiguous Doppler frequency. Furthermore, with the proposed signal model, the complex image properties can be completely described and analyzed. Finally, some numerical experiments are also provided to demonstrate the effectiveness of the proposed signal model and analysis.

A Maximum Likelihood Approach to Estimation of Vector Velocity in Doppler Radar Networks

Feb. 2012

In this paper, a vector velocity estimation approach based on the maximum likelihood technique operating on moment data within the overlapping area of a network of Doppler radars is presented. The relationships between the estimated vector velocity, the statistics of the measured signal, the characteristics of the observing geometry and volume, and the hardware and signal processing parameters are all derived. The most relevant error sources to the network measurements are derived and incorporated into the overall estimation process. The relationship between the measurement and estimation errors is identified, and exploited, so that estimation performance can be measured and, if necessary, improved through the means of error norm minimization. Techniques for mitigating errors in the synthesized reflectivity and velocity folding are presented as well. Results with error metrics are shown for several typical weather observation scenarios that include error sources and simulated data. Finally, it is shown how the technique may be used to provide useful information for the design and intercomparison of various Doppler radar network geometries.

Real-Time Vector Velocity Estimation in Doppler Radar Networks

Feb. 2012

In this paper, a real-time algorithm that operates on data provided by a network of weather radars in the form of moments is presented. The algorithm implementation, performance, and abilities are presented and discussed. As part of this paper, a methodology for mapping from native spherical coordinates to a common network grid is developed and demonstrated. Metrics which gauge the quality of mapping and quality of data are derived, and their use from a network point of view is demonstrated. The main product of the algorithm, which is an implementation of a vector velocity estimation technique based on a maximum likelihood approach, is shown, and its results are displayed, compared, and discussed. The application of a methodology for velocity unfolding and reflectivity display enhancement is presented as well.

Mapping Basal Melt Under the Northern Greenland Ice Sheet

Feb. 2012

Ice sheets in Greenland and Antarctica are based on continental rock and are coupled strongly to changes in global sea level. Their beds may be frozen or thawed, but it has not been known how much or where basal melt occurs, except in special cases where a borehole has reached the bed or where airborne radar has revealed subglacial lakes and reflective ice stream beds in Antarctica. We have used a previously published technique to detect subglacial melt water in the general case and have here applied it to radar-sounder data collected over northern and central Greenland. We have found extensive subglacial water along between 13% and 20% of the flight paths. This paper provides maps of the measured locations and probable extent of subglacial water.

Filtering and Segmentation of Polarimetric SAR Data Based on Binary Partition Trees

Feb. 2012

In this paper,we propose the use of binary partition trees (BPT) to introduce a novel region-based and multi-scale polarimetric SAR (PolSAR) data representation. The BPT structure represents homogeneous regions in the data at different detail levels. The construction process of the BPT is based, firstly, on a region model able to represent the homogeneous areas, and, secondly, on a dissimilarity measure in order to identify similar areas and define the merging sequence. Depending on the final application, a BPT pruning strategy needs to be introduced. In this paper, we focus on the application of BPT PolSAR data representation for speckle noise filtering and data segmentation on the basis of the Gaussian hypothesis, where the average covariance or coherency matrices are considered as a region model. We introduce and quantitatively analyze different dissimilarity measures. In this case, and with the objective to be sensitive to the complete polarimetric information under the Gaussian hypothesis, dissimilarity measures considering the complete covariance or coherency matrices are employed. When confronted to PolSAR speckle filtering, two pruning strategies are detailed and evaluated. As presented, the BPT PolSAR speckle filter defined filters data according to the complete polarimetric information. As shown, this novel filtering approach is able to achieve very strong filtering while preserving the spatial resolution and the polarimetric information. Finally, the BPT representation structure is employed for high spatial resolution image segmentation applied to coastline detection. The analyses detailed in this work are based on simulated, as well as on real PolSAR data acquired by the ESAR system of DLR and the RADARSAT-2 system.

A Nonlocal SAR Image Denoising Algorithm Based on LLMMSE Wavelet Shrinkage

Feb. 2012

We propose a novel despeckling algorithm for synthetic aperture radar (SAR) images based on the concepts of nonlocal filtering and wavelet-domain shrinkage. It follows the structure of the block-matching 3-D algorithm, recently proposed for additive white Gaussian noise denoising, but modifies its major processing steps in order to take into account the peculiarities of SAR images. A probabilistic similarity measure is used for the block-matching step, while the wavelet shrinkage is developed using an additive signal-dependent noise model and looking for the optimum local linear minimum-mean-square-error estimator in the wavelet domain. The proposed technique compares favorably w.r.t. several state-of-the-art reference techniques, with better results both in terms of signal-to-noise ratio (on simulated speckled images) and of perceived image quality.

Ambiguity Suppression by Azimuth Phase Coding in Multichannel SAR Systems

Feb. 2012

The current generation of spaceborne synthetic aperture radar (SAR) systems suffers from a tradeoff between the achievable spatial resolution and swath width. This has motivated intensive research both on more flexible SAR systems, using multiple transmit/receive channels, and on techniques for removing the ambiguities. Among these techniques, the azimuth phase coding (APC), recently proposed to suppress range ambiguities in conventional SAR systems, stands out for its negligible implementation complexity and its effectiveness for point and distributed ambiguities. This paper investigates the possibility of applying the APC technique to the new, forthcoming generation of multichannel SAR systems, based on digital beamforming on receive. The extension of APC to multichannel SAR systems is mathematically described. Specific merit figures are defined to quantify the APC performance. A numerical analysis is developed to characterize the influence on the APC behaviors of the main SAR system parameters. Finally, an example of APC performance is provided, by considering two multichannel SAR systems based on a planar and a reflector antenna.

SAR Imaging of Fractal Surfaces

Feb. 2012

A complete theoretical model for synthetic aperture radar (SAR) imaging of natural surfaces is introduced in this paper. The topography of the natural scenes is described via models derived from fractal geometry; scattering evaluations are performed via fractal scattering models appropriate to the employed fractal scene description. Scattering contributions are combined according to the SAR image impulse response function. The power spectral density of appropriate cuts of the SAR image are evaluated in closed form in terms of the surface fractal parameters. Our theoretical model is here conceptually assessed, analytically derived, graphically validated, numerically verified, and also tested on simulated SAR images. The introduced model allows defining innovative postprocessing inverse techniques to retrieve fractal parameters directly from SAR images.

Estimation of Aerosol Effective Radius by Multiwavelength Elastic Lidar

Feb. 2012

A new lidar algorithm is presented as part of a technique for estimating aerosol concentration and particle-size distribution (PSD). This technique uses a form of the extended Kalman filter (EKF), wherein the target aerosol is represented as a linear combination of basis-aerosols, so that the estimated PSD of the aerosol is a linear combination of the PSD of the individual basis-aerosols. The state vector of the filter contains the amplitudes of the basis-aerosols, eliminating the need for an intermediate step of estimating scattering coefficients. Point-sensor instruments and Mie scattering theory are used to establish the relationship between basis-aerosols and measured power. The algorithm is demonstrated using both synthetic test data and field measurements of biological and nonbiological aerosols. The estimated PSD allows straightforward calculation of parameters such as volume-fraction concentration and effective radius.

Automatic Stem Mapping Using Single-Scan Terrestrial Laser Scanning

Feb. 2012

The demand for detailed ground reference data in quantitative forest inventories is growing rapidly, e.g., to improve the calibration of the developed models of airborne-laser-scanning-based inventories. The application of terrestrial laser scanning (TLS) in the forest has shown great potential for improving the accuracy and efficiency of field data collection. This paper presents a fully automatic stem-mapping algorithm using single-scan TLS data for collecting individual tree information from forest plots. In this method, the stem points are identified by the spatial distribution properties of the laser points, the stem model is built up of a series of cylinders, and the location of the stem is estimated by the model. The experiment was performed on nine plots with 10-m radius. The stem-location maps measured in the field by traditional methods were used as the ground truth. The overall stem-mapping accuracy was 73%. The result shows that, in a relatively dense managed forest, the majority of stems can be located by the automatic algorithm. The proposed method is a general solution for stem locating where particular plot knowledge and data format are not required.

IEEE Copyright Form

Feb. 2012

IEEE Transactions on Geoscience and Remote Sensing information for authors

Feb. 2012

IEEE Transactions on Geoscience and Remote Sensing institutional listings

Feb. 2012

Geoscience and Remote Sensing, IEEE Transactions on - new TOC

Front cover

IEEE Transactions on Geoscience and Remote Sensing publication information

Table of Contents

Introduction to Special Section on Space Technology

TGRS Space Technology Special Section List of Reviewers

Near-Space Vehicle-Borne SAR With Reflector Antenna for High-Resolution and Wide-Swath Remote Sensing

Improved Range Ambiguity Performance in Quad-Pol SAR

Geolocation of Argus Flight Data

An FPGA-Based Hardware Implementation of Configurable Pixel-Level Color Image Fusion

FPGA Implementation of the N-FINDR Algorithm for Remotely Sensed Hyperspectral Image Analysis

A Saturated Light Correction Method for DMSP/OLS Nighttime Satellite Imagery

Markov Chain CFAR Detection for Polarimetric Data Using Data Fusion

Uncertainty Analysis of Neural-Network-Based Aerosol Retrieval

Spectral Downscaling of Integrated Water Vapor Fields From Satellite Infrared Observations

Evaluation and Wind Speed Dependence of MODIS Aerosol Retrievals Over Open Ocean

Experimental Detection and Characterization of Brillouin Precursor Through Loamy Soil at Microwave Frequencies

Improving Spatial Soil Moisture Representation Through Integration of AMSR-E and MODIS Products

Application of QuikSCAT Backscatter to SMAP Validation Planning: Freeze/Thaw State Over ALECTRA Sites in Alaska From 2000 to 2007

Comparison of Ray-Tracing Packages for Troposphere Delays

A Graphic Representation of the Necessary Condition for the MAFA Method

A Variable-Resolution Probabilistic Three-Dimensional Model for Change Detection

A Bicriteria-Optimization-Approach-Based Dimensionality-Reduction Model for the Color Display of Hyperspectral Images

A Simple and Robust Feature Point Matching Algorithm Based on Restricted Spatial Order Constraints for Aerial Image Registration

Coupled Nonnegative Matrix Factorization Unmixing for Hyperspectral and Multispectral Data Fusion

Ground Moving Target Signal Analysis in Complex Image Domain for Multichannel SAR

A Maximum Likelihood Approach to Estimation of Vector Velocity in Doppler Radar Networks

Real-Time Vector Velocity Estimation in Doppler Radar Networks

Mapping Basal Melt Under the Northern Greenland Ice Sheet

Filtering and Segmentation of Polarimetric SAR Data Based on Binary Partition Trees

A Nonlocal SAR Image Denoising Algorithm Based on LLMMSE Wavelet Shrinkage

Ambiguity Suppression by Azimuth Phase Coding in Multichannel SAR Systems

SAR Imaging of Fractal Surfaces

Estimation of Aerosol Effective Radius by Multiwavelength Elastic Lidar

Automatic Stem Mapping Using Single-Scan Terrestrial Laser Scanning

IEEE Copyright Form

IEEE Transactions on Geoscience and Remote Sensing information for authors

IEEE Transactions on Geoscience and Remote Sensing institutional listings