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Geoscience and Remote Sensing, IEEE Transactions on

Issue 4 • Date April 2012

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Displaying Results 1 - 25 of 33
  • [Front cover]

    Page(s): C1
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    Freely Available from IEEE
  • IEEE Transactions on Geoscience and Remote Sensing publication information

    Page(s): C2
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  • Table of contents

    Page(s): 1013 - 1014
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  • Assimilation of Oceansat-2-Scatterometer-Derived Surface Winds in the Weather Research and Forecasting Model

    Page(s): 1015 - 1021
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (839 KB) |  | HTML iconHTML  

    This paper describes, for the first time, the impact of Oceansat-2 scatterometer (OSCAT) surface winds in the Weather Research and Forecasting (WRF) 3-D variational (3-D-Var) assimilation system. Before using OSCAT winds into WRF assimilation system, we compared OSCAT surface wind retrievals against National Centers for Environmental Prediction analyzed winds, Advanced Scatterometer retrievals, and buoy-measured winds. After the initial assessment of the quality of the OSCAT winds, the control (CNT) (without OSCAT) as well as experimental (which assimilated OSCAT surface winds) runs were made for 48 h starting daily at 0600 Universal Time Coordinated (UTC) during July 2010. The assimilation experiments demonstrated positive impact of OSCAT winds on both the analysis state as well as subsequent short-term forecasts of surface winds. Compared to CNT run, the assimilation of the OSCAT winds improved the surface wind analysis as large as 25%, when compared with Advanced Microwave Scanning Radiometer (AMSR-E) measured winds. The assimilation of OSCAT winds also showed small, but positive, impact on the forecast (particularly later hours of forecast) of midtropospheric moisture, temperature, and upper tropospheric winds. Compared to the CNT run, the assimilation of OSCAT winds improved precipitation forecast for moderate to heavy rainfall thresholds when validated against Tropical Rainfall Measuring Mission precipitation. View full abstract»

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  • Sea Surface Salinity and Wind Retrieval Using Combined Passive and Active L-Band Microwave Observations

    Page(s): 1022 - 1032
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    This paper describes an algorithm to simultaneously retrieve ocean surface salinity and wind from combined passive/active L-band microwave observations of sea surfaces. The algorithm takes advantage of the differing response of brightness temperatures and radar backscatter to salinity, wind speed, and direction. The algorithm minimizes the least square error (LSE) measure, signifying the difference between measurements and model functions of brightness temperatures and radar backscatter. Three LSE measures with different measurement combinations are tested. One of the LSE measures uses passive microwave data only with retrieval errors reaching 2 psu for salinity and 2 m/s for wind speed. The second LSE measure uses both passive and active microwave data for vertical and horizontal polarizations. The addition of active microwave data significantly improves the retrieval accuracy by about a factor of five. To mitigate the impact of Faraday rotation on satellite observations, we propose the third LSE measure using measurement combinations invariant under the Faraday rotation. For Aquarius, the expected root-mean-square SSS error will be less than 0.2 psu for low winds and increases to 0.3 psu at 25-m/s wind speed for warm waters, and the accuracy of retrieved wind speed will be high (about 1-2 m/s or lower). Our results suggest that combining passive and active microwave observations will allow retrieval of sea surface salinity along with the wind speed and direction. In particular, the LSE measure invariant under the Faraday rotation will be directly applicable to spaceborne missions, such as the NASA Aquarius and Soil Moisture Active Passive missions. View full abstract»

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  • A Sensor Package for Ice Surface Observations Using Small Unmanned Aircraft Systems

    Page(s): 1033 - 1047
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    A suite of sensors has been assembled to map surface elevation and topography with fine resolution from small unmanned aircraft systems. The sensor package consists of a light detecting and ranging (LIDAR) instrument, an inertial measurement unit (IMU), a Global Positioning System (GPS) module, and digital still and video cameras. The system has been utilized to map ice sheet topography in Greenland and to measure sea ice freeboard and roughness in Fram Strait off the coast of Svalbard and in the Southern Ocean near McMurdo, Antarctica. The elevation measurement accuracy is found to be <; 10 cm (1σ) when short-baseline differential GPS processing is used to position the aircraft, and IMU data are used to correct for off-nadir pointing of the LIDAR. The system is optimized to provide coincident surface topography measurements and imagery of ice sheets, glaciers, and sea ice, and it has the potential to become a widely distributed observational resource to complement manned-aircraft and satellite missions. View full abstract»

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  • A Remote Sensing Approach for Landslide Hazard Assessment on Engineered Slopes

    Page(s): 1048 - 1056
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (689 KB) |  | HTML iconHTML  

    Earthworks such as embankments and cuttings are integral to road and rail networks but can be prone to instability, necessitating rigorous and continual monitoring. To date, the potential of remote sensing for earthwork hazard assessment has been largely overlooked. However, techniques such as airborne laser scanning (ALS) are now ripe for addressing these challenges. This research presents the development of a novel hazard assessment strategy, combining high-resolution remote sensing with a numerical modeling approach. The research was implemented at a railway test site located in northern England, U.K.; ALS data and multispectral aerial imagery facilitated the determination of key slope stability variables, which were then used to parameterize a coupled hydrological-geotechnical model, in order to simulate slope behavior under current and future climates. A software toolset was developed to integrate the core elements of the methodology and determine resultant slope failure hazard which could then be mapped and queried within a geographical information system environment. Results indicate that the earthworks are largely stable, which is in broad agreement with the management company's slope hazard grading data, and in terms of morphological analysis, the remote methodology was able to correctly identify 99% of earthworks classed as embankments and 100% of cuttings. The developed approach provides an effective and practicable method for remotely quantifying slope failure hazard at fine spatial scales (0.5 m) and for prioritizing and reducing on-site inspection. View full abstract»

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  • A RADARSAT-2 Quad-Polarized Time Series for Monitoring Crop and Soil Conditions in Barrax, Spain

    Page(s): 1057 - 1070
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    An analysis of the sensitivity of synthetic aperture radar (SAR) backscatter (σo) to crop and soil conditions was conducted using 57 RADARSAT-2 C-band quad-polarized SAR images acquired from April to September 2009 for large fields of wheat, barley, oat, corn, onion, and alfalfa in Barrax, Spain. Preliminary results showed that the cross-polarized σHVo was particularly useful for monitoring both crop and soil conditions and was the least sensitive to differences in beam incidence angle. The greatest separability of barley, corn, and onion occurred in spring after the barley had been harvested or in the narrow time window associated with grain crop heading when corn and onion were still immature. The time series of σo offered reliable information about crop growth stage, such as jointing and heading in grain crops and leaf growth and reproduction in corn and onion. There was a positive correlation between σo and the Normalized Difference Vegetation Index for onion and corn but not for all crops, and the impact of view direction and incidence angle on the time series was minimal compared to the signal response to crop and soil conditions. Related to planning for future C-band SAR missions, we found that quad-polarization with image acquisition frequency from 3-6 days was best suited for distinguishing crop types and for monitoring crop phenology, single- or dual-polarization with an acquisition frequency of 3-6 days was sufficient for mapping crop green biomass, and single- or dual-polarization with daily image acquisition was necessary to capture rapid changes in soil moisture condition. View full abstract»

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  • Impact of Conifer Forest Litter on Microwave Emission at L-Band

    Page(s): 1071 - 1084
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1962 KB) |  | HTML iconHTML  

    This study reports on the utilization of microwave modeling, together with ground truth, and L-band (1.4-GHz) brightness temperatures to investigate the passive microwave characteristics of a conifer forest floor. The microwave data were acquired over a natural Virginia Pine forest in Maryland by a ground-based microwave active/passive instrument system in 2008/2009. Ground measurements of the tree biophysical parameters and forest floor characteristics were obtained during the field campaign. The test site consisted of medium-sized evergreen conifers with an average height of 12 m and average diameters at breast height of 12.6 cm. The site is a typical pine forest site in that there is a surface layer of loose debris/needles and an organic transition layer above the mineral soil. In an effort to characterize and model the impact of the surface litter layer, an experiment was conducted on a day with wet soil conditions, which involved removal of the surface litter layer from one half of the test site while keeping the other half undisturbed. The observations showed detectable decrease in emissivity for both polarizations after the surface litter layer was removed. A first-order radiative transfer model of the forest stands including the multilayer nature of the forest floor in conjunction with the ground truth data are used to compute forest emission. The model calculations reproduced the major features of the experimental data over the entire duration, which included the effects of surface litter and ground moisture content on overall emission. Both theory and experimental results confirm that the litter layer increases the observed canopy brightness temperature and obscure the soil emission. View full abstract»

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  • A Changing-Weight Filter Method for Reconstructing a High-Quality NDVI Time Series to Preserve the Integrity of Vegetation Phenology

    Page(s): 1085 - 1094
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    Time-series data of normalized difference vegetation index (NDVI), derived from satellite sensors, can be used to support land-cover change detection and phenological interpretations, but further analysis and applications are hindered by residual noise in the data. As an alternative to a number of existing algorithms developed to compensate for such noise, we develop a simple but computationally efficient method (which we call the changing-weight filter method) to reconstruct a high-quality NDVI time series. The new algorithm consists of two major procedures: (1) detecting the local maximum/minimum points in a growth cycle along an NDVI temporal profile based on a mathematical morphology algorithm and a rule-based decision process and (2) filtering an NDVI time series with a three-point changing-weight filter. This method is tested at 470 test points for 55 vegetation types and a test region in China using a 250-m 16-day Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI product. Comparing our results to those of three other well-known methods-asymmetric Gaussian function fitting, double logistic function fitting, and Savitzky-Golay filtering-the new method has many of the advantages of existing methods, while in some cases, the changing-weight filter method more effectively preserves the curve shape as well as the timing and the amplitude of the local maxima/minima in the NDVI time series for a broad range of phenologies. Moreover, the response of the filtering algorithm is relatively insensitive to the exact values of its design parameters, making the new method more flexible and effective in adjusting to fit a variety of classes of NDVI time series. View full abstract»

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  • Magnetic Anomaly Detection Using High-Order Crossing Method

    Page(s): 1095 - 1103
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    Magnetic anomaly detection (MAD) is a passive method used to detect visually obscured ferromagnetic objects by revealing the anomalies in the ambient Earth magnetic field. In this paper, we propose a method for MAD employing the high-order crossing (HOC) approach, which relies on the magnetic background nature. HOC is an alternative method for spectral analysis using zero-crossing count, also enabling signal discrimination. Tests with real-world recorded magnetic signals show high detection probability even for low signal-to-noise ratio. The high detection probability, together with a simple implementation and low power consumption, makes the HOC method attractive for real-time MAD applications such as intruder detection and for research on an earthquake magnetic precursor. View full abstract»

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  • Doppler Spectra of Microwave Scattering Fields From Nonlinear Oceanic Surface at Moderate- and Low-Grazing Angles

    Page(s): 1104 - 1116
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    Results of microwave radar Doppler spectra from 1-D nonlinear ocean surface at moderate- and low-grazing angles are calculated by the composite surface scattering model. For the large-scale undulating surface description, the narrow-band Lagrange model is used, which takes into account the vertical and horizontal skewnesses. Moreover, the shadow and the curvature effects of large-scale waves on the Doppler spectra are also considered in our calculations. Comparisons of computed curves with experimentally measured Doppler spectra at different incidence angles and at various wind speeds show that the simulated results can fit the measured data well at moderate incident angles. From the simulations, we also find that the hydrodynamic modulation and the horizontal skewness of the large-scale waves can induce remarkable influence on Doppler shift. In addition, when the shadow and the curvature effects of large-scale waves are considered in the calculations, the Doppler shifts grow more quickly and the spectral widths become narrower at low-grazing angles, and this is consistent with the numerical results given by Toporkov in the nonlinear surface case. The conclusions obtained in this work seem promising for better understanding the properties of time-dependent radar echoes from oceanic surfaces. View full abstract»

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  • Superresolution Differential Tomography: Experiments on Identification of Multiple Scatterers in Spaceborne SAR Data

    Page(s): 1117 - 1129
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    Interest is growing in the application of coherent processing of synthetic aperture radar (SAR) data to the monitoring of complex urban or infrastructure areas. However, such scenarios are characterized by the layover phenomenon, in the presence of which conventional interferometric SAR techniques degrade or cannot operate. As a consequence, to monitor reliably a high number of ground structures, the identification, i.e., the detection and height and deformation velocity estimation, of both single and multiple scatterers interfering in the same SAR cell can be a key step. This issue is addressed here by means of differential tomography (Diff-Tomo), a recent multibaseline-multitemporal generalized interferometric framework which allows to resolve multiple moving scatterers at different heights in the same cell. In particular, superresolution adaptive Diff-Tomo is extensively tested and augmented with a new information extraction algorithm for the automated identification of the multiple scatterers. Experiments have been carried out with real C-band spaceborne data over urban areas; corresponding results are shown and discussed. View full abstract»

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  • A Three-Dimensional Adaptive Integral Method for Scattering From Structures Embedded in Layered Media

    Page(s): 1130 - 1139
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    A 3-D extension of the adaptive integral method (AIM) is presented for fast analysis of scattering from electrically large perfect electrically conducting structures embedded inside a single layer of a planar layered medium. The proposed scheme accelerates the iterative method-of-moments (MOM) solution of the combined-field integral equation by employing a 3-D auxiliary regular grid. It uses the auxiliary grid to execute the standard four-stage AIM procedure; unlike the procedure for free space, two different sets of matrices are obtained for the AIM propagation stage by decomposing the Green functions to terms that are in convolution or correlation form in the stratification direction. These matrices are in (three level) block-Toeplitz and Hankel-(two level)-block-Toeplitz forms and can be multiplied by using 3-D FFTs. The dominant computational costs of the scheme are the evaluation of O(N) different layered-medium Green functions, which is accelerated by extracting asymptotic terms and using interpolation tables, and the matrix multiplications in the propagation stage, which require only O(NC log NC) per iteration; these should be contrasted to the O(N2) Green function evaluations and O(N2) operations per iteration required by the classical MOM. Here, NC denotes the number of nodes on the auxiliary grid, and N denotes the number of degrees of freedom of the surface current density. Numerical results validate the proposed method's complexity, demonstrate its accuracy for several large-scale structures in layered media, and compare its computational costs to those of its counterpart for free space. View full abstract»

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  • Development of the Landsat Data Continuity Mission Cloud-Cover Assessment Algorithms

    Page(s): 1140 - 1154
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    The upcoming launch of the Operational Land Imager (OLI) will start the next era of the Landsat program. However, the Automated Cloud-Cover Assessment (CCA) (ACCA) algorithm used on Landsat 7 requires a thermal band and is thus not suited for OLI. There will be a thermal instrument on the Landsat Data Continuity Mission (LDCM)-the Thermal Infrared Sensor-which may not be available during all OLI collections. This illustrates a need for CCA for LDCM in the absence of thermal data. To research possibilities for full-resolution OLI cloud assessment, a global data set of 207 Landsat 7 scenes with manually generated cloud masks was created. It was used to evaluate the ACCA algorithm, showing that the algorithm correctly classified 79.9% of a standard test subset of 3.95 109 pixels. The data set was also used to develop and validate two successor algorithms for use with OLI data-one derived from an off-the-shelf machine learning package and one based on ACCA but enhanced by a simple neural network. These comprehensive CCA algorithms were shown to correctly classify pixels as cloudy or clear 88.5% and 89.7% of the time, respectively. View full abstract»

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  • Very High Resolution Multiangle Urban Classification Analysis

    Page(s): 1155 - 1170
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4514 KB) |  | HTML iconHTML  

    The high-performance camera control systems carried aboard the DigitalGlobe WorldView satellites, WorldView-1 and WorldView-2, are capable of rapid retargeting and high off-nadir imagery collection. This provides the capability to collect dozens of multiangle very high spatial resolution images over a large target area during a single overflight. In addition, WorldView-2 collects eight bands of multispectral data. This paper discusses the improvements in urban classification accuracy available through utilization of the spatial and spectral information from a WorldView-2 multiangle image sequence collected over Atlanta, GA, in December 2009. Specifically, the implications of adding height data and multiangle multispectral reflectance, both derived from the multiangle sequence, to the textural, morphological, and spectral information of a single WorldView-2 image are investigated. The results show an improvement in classification accuracy of 27% and 14% for the spatial and spectral experiments, respectively. Additionally, the multiangle data set allows the differentiation of classes not typically well identified by a single image, such as skyscrapers and bridges as well as flat and pitched roofs. View full abstract»

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  • Keypoint-Based Analysis of Sonar Images: Application to Seabed Recognition

    Page(s): 1171 - 1184
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2010 KB) |  | HTML iconHTML  

    In this paper, we address seabed characterization and recognition in sonar images using keypoint-based approaches. Keypoint-based texture recognition has recently emerged as a powerful framework to address invariances to contrast change and geometric distortions. We investigate here to which extent keypoint-based techniques are relevant for sonar texture analysis which also involves such invariance issues. We deal with both the characterization of the visual signatures of the keypoints and the spatial patterns they form. In this respect, spatial statistics are considered. We report a quantitative evaluation for sonar seabed texture data sets comprising six texture classes such as mud, rock, and gravely sand. We clearly demonstrate the improvement brought by keypoint-based techniques compared to classical features used for sonar texture analysis such as cooccurrence and Gabor features. In this respect, we demonstrate that the joint characterization of the visual signatures of the visual keypoints and their spatial organization reaches the best recognition performances (about 97% of correct classification w.r.t. 70% and 81% using cooccurrence and Gabor features). Furthermore, the combination of difference of Gaussian keypoints and scale-invariant feature transform descriptors is recommended as the most discriminating keypoint-based framework for the analysis of sonar seabed textures. View full abstract»

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  • Locality-Preserving Dimensionality Reduction and Classification for Hyperspectral Image Analysis

    Page(s): 1185 - 1198
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    Hyperspectral imagery typically provides a wealth of information captured in a wide range of the electromagnetic spectrum for each pixel in the image; however, when used in statistical pattern-classification tasks, the resulting high-dimensional feature spaces often tend to result in ill-conditioned formulations. Popular dimensionality-reduction techniques such as principal component analysis, linear discriminant analysis, and their variants typically assume a Gaussian distribution. The quadratic maximum-likelihood classifier commonly employed for hyperspectral analysis also assumes single-Gaussian class-conditional distributions. Departing from this single-Gaussian assumption, a classification paradigm designed to exploit the rich statistical structure of the data is proposed. The proposed framework employs local Fisher's discriminant analysis to reduce the dimensionality of the data while preserving its multimodal structure, while a subsequent Gaussian mixture model or support vector machine provides effective classification of the reduced-dimension multimodal data. Experimental results on several different multiple-class hyperspectral-classification tasks demonstrate that the proposed approach significantly outperforms several traditional alternatives. View full abstract»

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  • Generalized Detection Fusion for Hyperspectral Images

    Page(s): 1199 - 1205
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (337 KB) |  | HTML iconHTML  

    The purpose of this paper is to introduce a general type of detection fusion that allows combining a set of basic detectors into one more versatile detector. The fusion can be performed based on the spectral information contained in a pixel, the global characteristics of the background and target spaces, as well as spatial local information. As an example of generalized fusion, we introduce a new class of detectors called the directional segmented matched filters (DSMFs). We then concentrate on the more basic type of fusion that does not use the spatial local information. Our goal is to build a theoretical foundation for the future more sophisticated detectors. Within this setup, we define max-min and min-max types of fusion, which turn out to be equivalent to the geometric approach to continuum fusion already introduced in the literature. Nevertheless, this new framework allows natural formulation of other types of approaches, such as discrete fusion, without the continuity assumption. This new formalism also allows formulation of a general theorem about the relationship between the max-min and min-max detectors. We also provide experimental results that demonstrate the benefits of the new approach. We compare two new detectors with the global matched filter using two different targets in an AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) image. The results show that various forms of the DSMFs dominate depending on the target type. View full abstract»

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  • Rotation-Invariant Object Detection of Remotely Sensed Images Based on Texton Forest and Hough Voting

    Page(s): 1206 - 1217
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    The Hough forest method is an effective method for object detection in ground-shot images that has received increasing research attention. However, this method lacks the ability to detect objects with arbitrary orientations. This largely constrains the method from being used in detecting geospatial objects from remotely sensed (RS) images since geospatial objects can have many different orientations. In order to achieve rotation invariance and compensate the associated loss of discriminative power, this paper presents a novel color-enhanced rotation-invariant Hough forest (CRIHF) method for detecting geospatial objects in RS images. In our method, we propose to train a Pose-Estimation-based Rotation-invariant Texton Forest (PE-RTF) which first uses dominant gradient orientations to align local image patches. The orientations are then jointly used with coordinates in Hough voting to detect object position. In order to increase discriminative power, Texton Forest is used in codebook generation. Moreover, theoretically sound color-invariant gradients are employed. By rotating split functions rather than image patches in the RTF and sparsely accumulating Hough votes on grid points, computational times can be reduced by two orders of magnitude. The evaluation of the CRIHF method on a data set containing 525 airplanes and a second data set containing 68 residential buildings shows that our method is rotation invariant and robust. The detector achieves around 90% recall rate on both data sets. Experiments also show that our method is noise resistant and can achieve a decent detection performance at a high level (30%) of “salt and pepper” impulsive noise. View full abstract»

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  • Through-the-Wall Sensing of Personnel Using Passive Bistatic WiFi Radar at Standoff Distances

    Page(s): 1218 - 1226
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (611 KB) |  | HTML iconHTML  

    In this paper, we investigate the feasibility of uncooperatively and covertly detecting people moving behind walls using passive bistatic WiFi radar at standoff distances. A series of experiments was conducted which involved personnel targets moving inside a building within the coverage area of a WiFi access point. These targets were monitored from outside the building using a 2.4-GHz passive multistatic receiver, and the data were processed offline to yield range and Doppler information. The results presented show the first through-the-wall (TTW) detections of moving personnel using passive WiFi radar. The measured Doppler shifts agree with those predicted by bistatic theory. Further analysis of the data revealed that the system is limited by the signal-to-interference ratio (SIR), and not the signal-to-noise ratio. We have also shown that a new interference suppression technique based on the CLEAN algorithm can improve the SIR by approximately 19 dB. These encouraging initial findings demonstrate the potential for using passive WiFi radar as a low-cost TTW detection sensor with widespread applicability. View full abstract»

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  • Ionospheric Artifacts in Simultaneous L-Band InSAR and GPS Observations

    Page(s): 1227 - 1239
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2087 KB) |  | HTML iconHTML  

    Phase artifacts in interferometric synthetic aperture radar (InSAR) images frequently degrade the interpretability of the phase and correlation signatures of terrain. Often, these distortions are attributed to spatially variable ionospheric propagation delays at two different SAR acquisition times. We present here L-band InSAR data from Iceland, California, and Hawaii. The California and Hawaii interferograms show no significant ionospheric artifacts, while the Iceland interferogram shows a maximum misregistration of three pixels in the azimuth direction, which leads to severe phase decorrelation artifacts in the InSAR image. We relate the misregistration of complex pixels seen in the interferograms to the gradient of the ionospheric total electron content (TEC) observed by global positioning system (GPS) data and confirm that indeed the phase artifacts in the Iceland interferogram are due to dispersive ionospheric propagation rather than other decorrelation factors such as neutral atmospheric delays. We develop a method to measure the spatial TEC variation at synthetic aperture length scales using dual-frequency GPS carrier phase data. We solve for the GPS data ambiguities using a low-resolution ionosphere reference derived from either available ionospheric observations or the GPS carrier phase data themselves. GPS observations show directly the level of ionospheric variability, and the spatial TEC gradient as observed by GPS predicts the misregistration of complex pixels in interferograms in all three areas. This confirmation of the cause of the image artifacts suggests that they can be routinely corrected from the InSAR data alone, provided that the sensor measures the change in TEC along the radar swath. View full abstract»

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  • Detection and Mitigation of Second-Trip Echo in Polarimetric Weather Radar Employing Random Phase Coding

    Page(s): 1240 - 1253
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    This study presents a new identification and mitigation scheme of second trip contamination for pulsed Doppler polarimetric weather radars with the ability of random phase coding. This scheme can be easily implemented in a magnetron radar without any hardware changes. For relatively weak contamination, identification and mitigation are based on a multilag processing method, which uses multiple lags of both the auto- and cross-correlation functions to estimate radar moments. For relatively strong contamination, instantaneous phase variations of horizontal and vertical polarization channels are combined into a simple fuzzy-logic scheme to complete the identification. Data from the C-band OU-PRIME radar are used to demonstrate the effectiveness of the proposed scheme for identification and mitigation of second-trip echoes. View full abstract»

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  • Vital Sign Detection Method Based on Multiple Higher Order Cumulant for Ultrawideband Radar

    Page(s): 1254 - 1265
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    The research on vital information extraction meets a lot challenges due to the narrow bandwidth and low signal-to-noise ratio of a vital sign in real environment. In this paper, a novel noncontract vital sign detection method based on multiple higher order cumulant is presented. According to the characteristic of vital sign for impulse ultrawideband radar, the quasi-periodic reflected echo in slow-time is analyzed. The novel method is theoretically deduced from fourth-order cumulant. It is proved to be better than the reference fast Fourier transform method by simulation and experiment. By using the new method, the range position and frequency information of life can be extracted accurately and automatically. View full abstract»

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  • Validating Subglacial Volcanic Eruption Using Ground-Based C-Band Radar Imagery

    Page(s): 1266 - 1282
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    The main phase of the moderately sized November 2004 eruption of the Grímsvötn volcano, located in the center of the 8100 km2 Vatnajökull glacier, was monitored by the Icelandic Meteorological Office C-band weather radar in Keflavík, 260 km west of the volcano. The eruption plume reached a height of 6-10 km relative to the vent. The distribution of the most distal tephra was measured in the autumn of 2004, while the deposition on the glacier was mapped in the summers of 2005 and 2006. The tephra formed a well-defined layer on the glacier in the region north and northeast of the craters. The total mass of the tephra layer is quantitatively compared with the retrieved values, obtained from an improved version of the volcanic ash radar retrieval (VARR) algorithm. VARR was statistically calibrated with ground-based ash size distribution samples, taken at Vatnajökull, and by taking into account both antenna beam occlusion and wind-driven plume advection. The latter was implemented by using a space-time image phase-based cross-correlation technique. Accuracy of the weather radar records was also reviewed, noting that a large variability in the plume height estimation may be obtained using different approaches. The comparisons suggest that, at least for this subglacial eruption, the surface tephra mass, estimated by using the VARR inversion approach, is in a fairly good agreement with in situ measurements in terms of spatial extension, distribution, and amount. View full abstract»

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (TGRS) is a monthly publication that focuses on the theory, concepts, and techniques of science and engineering as applied to sensing the land, oceans, atmosphere, and space; and the processing, interpretation, and dissemination of this information.

 

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Editor-in-Chief
Antonio J. Plaza
University of Extremadura