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Geoscience and Remote Sensing, IEEE Transactions on

Issue 10  Part 2 • Date Oct. 2012

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  • [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): 3929 - 3930
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  • Enhancement of Satellite Precipitation Estimation via Unsupervised Dimensionality Reduction

    Page(s): 3931 - 3940
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1082 KB) |  | HTML iconHTML  

    A methodology to enhance satellite precipitation estimation using unsupervised dimensionality reduction (UDR) techniques is developed. This enhanced technique is an extension to the precipitation estimation from remotely sensed imagery using an artificial neural network (PERSIANN) and cloud classification system (CCS) method (PERSIANN-CCS) enriched using wavelet features combined with dimensionality reduction. Cloud-top brightness temperature measurements from the Geostationary Operational Environmental Satellite (GOES)-12 are used for precipitation estimation at 4 km × 4 km spatial resolutions every 30 min. The study area in the continental U.S. covers parts of Louisiana, Arkansas, Kansas, Tennessee, Mississippi, and Alabama. Based on quantitative measures, root mean square error and Heidke skill score (HSS), the results show that the UDR techniques can improve the precipitation estimation accuracy. In addition, the independent component analysis is shown to have better performance than other UDR techniques; and in some cases, it achieves 10% improvement in the HSS. View full abstract»

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  • A New Type of Space Telescope for Observation of Extreme Lightning Phenomena in the Upper Atmosphere

    Page(s): 3941 - 3949
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1695 KB) |  | HTML iconHTML  

    A new type of space telescope with a 3 mm × 3 mm Micro-Electro-Mechanical System (MEMS) micromirror array has been fabricated and launched into space. This telescope has unique features: a wide field of surveillance view, and fast zoom-in and tracking capabilities. Although the micromirror array area is small, the space telescope was capable of observing the space-time development of extreme lightning in the upper atmosphere. It fulfilled its purpose by proving the principles of a space telescope. The concept and technologies used in this telescope can be extended to large MEMS space telescopes for future missions for earth and space science, including gamma ray bursts and ultra high energy cosmic rays. The performance of the space telescope during the ground test before launch as well as its performance in space are here presented to demonstrate the fast zoom-in and tracking capabilities of the telescope. View full abstract»

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  • Retrieval of Soil Salt Content From an Integrated Approach of Combining Inversed Reflectance Model and Regressions: An Experimental Study

    Page(s): 3950 - 3957
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (484 KB) |  | HTML iconHTML  

    Monitoring soil salinization has been a difficult process in arid lands due their large spatial and temporal variability. Hyperspectral remote sensing has offered a potential for faster detection of salinization process but mostly from empirical approaches. In this paper, an integrated approach combining model inversion and empirical regressions has been proposed for soil salt content (SSC) estimation from hyperspectral information obtained from controlled laboratory experiments. All soil samples were artificially salinized using Na2SO4, NaCl, and Na2CO3 (99% purity) salts to different levels and to different soil-moisture conditions, since soil moisture often jointly affects reflectance spectra with SSC. Hapke model was calibrated and validated for its simulation on soil reflectance and showed good agreements with measured data. The optimal values of single scattering albedo that was inversely retrieved from the Hapke model had good relationships with SSC at 2000-2200 nm for each treatment even under various soil-moisture conditions. Taking usage of these findings, the integrated approach obtained high accuracies on SSC estimations with R2's of 0.90, 0.86, and 0.72 and slightly dropped R2 's of 0.89, 0.81, and 0.67 for NaCl-, Na2SO4-, and Na2CO3-type saline soils under respective dry and wet conditions. The R2 decreased to 0.55 and 0.53 for dry and wet soils when salt types were ignored. The integrated approach provides a novel as well as an efficient way for SSC estimation from reflected spectra, and hence, we foresee its potential applications for large-scale SSC mapping from reflectance measurements. View full abstract»

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  • Computational-Geometry-Based Retrieval of Effective Leaf Area Index Using Terrestrial Laser Scanning

    Page(s): 3958 - 3969
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1135 KB) |  | HTML iconHTML  

    Quantifying the 3-D forest canopy structure and leaf area index of an individual tree or a forest stand is challenging. The canopy structural information implicitly contained within point cloud data (PCD) generated from terrestrial laser scanning (TLS) makes it possible to characterize directly the spatial distribution of foliage elements. In this paper, a new voxel-based method titled “point cloud slicing” is presented to retrieve the biophysical characteristics of the forest canopy including extinction coefficient, gap fraction, overlapping effect, and effective leaf area (ELA) from PCD. These extractions were performed not only from the whole canopy but also from layers of the canopy to depict the distribution patterns of foliage elements within the canopy. The results showed that the TLS-based ELA estimation method could explain 88.7% (rmse = 0.007, p <; 0.001, and n = 30) variation of the destructive-sample-based leaf area measurement results. It was found that the sampling resolution was a key parameter in defining the dimension of a single voxel. Furthermore, the TLS-based method can also serve as a calibration tool for airborne laser scanning application with ground sampling. View full abstract»

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  • Leaf Orientation Retrieval From Terrestrial Laser Scanning (TLS) Data

    Page(s): 3970 - 3979
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (582 KB) |  | HTML iconHTML  

    Tree leaf orientation, including the distribution of the inclinational and azimuthal angles in the canopy, is an important attribute of forest canopy architecture and is critical in determining the within and below canopy solar radiation regimes. Characterizing leaf orientation is a key step to the retrieval of leaf area index (LAI) based on remotely sensed data, particularly discrete point data such as that provided by light detection and ranging. In this paper, we present a new method that indirectly and nondestructively retrieves foliage elements' orientation and distribution from point cloud data (PCD) obtained using a terrestrial laser scanning (TLS) approach. An artificial tree was used to develop the method using total least square fitting techniques to reconstruct the normal vectors from the PCD. The method was further validated on live tree crowns. An equation with a single parameter for characterizing the leaf angular distribution of crowns was developed. The TLS-based algorithm captures 97.4% (RMSE = 1.094 degrees, p <; 0.001) variation of the leaf inclination angle compared to manual measurements for an artificial tree. When applied to a live tree seedling and a mature tree crown, the TLS-based algorithm predicts 78.51% (RMSE = 1.225 degrees, p <; 0.001) and 57.28% (RMSE = 4.412 degrees, p <; 0.001) of the angular variability, respectively. Our results indicate that occlusion and noisy points affect the accuracy of normal vector estimation. Most importantly, this work provides a theoretical foundation for retrieving LAI from PCD obtained with a TLS. View full abstract»

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  • Generating 275-m Resolution Land Surface Products From the Multi-Angle Imaging SpectroRadiometer Data

    Page(s): 3980 - 3990
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (807 KB) |  | HTML iconHTML  

    This paper shows how to reconstruct the original 275-m resolution data of the Multi-angle Imaging SpectroRadiometer (MISR) instrument in the 24 spectrodirectional global mode channels that are spatially averaged to 1.1 km on-board the Terra platform, with negligible loss of information relative to images acquired in native-resolution local mode. Standard approaches to improve the spatial resolution of products rely on one (typically panchromatic) high-resolution (HR) image to sharpen multiple spectral images. In the case of the MISR-HR package described here, three of the 12 available HR channels are combined to regenerate each of the 24 reduced-resolution channel to its native resolution. The accurate and rigorously reconstructed spectral bidirectional reflectance data allow sensitive and physically meaningful land surface attributes to be recovered at a spatial resolution appropriate to document the spatial heterogeneity of the land surface and relevant for climate and environment studies. MISR has been in continuous operation since February 2000 and provides global coverage in at most nine days (depending on latitude). This technique allows the generation of quantitative information to monitor change and model ecosystem function virtually anywhere and at any time during the last decade. The potential is demonstrated for a savanna landscape in South Africa. View full abstract»

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  • Improved Models of Soil Emission for Use in Remote Sensing of Soil Moisture

    Page(s): 3991 - 3999
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (298 KB) |  | HTML iconHTML  

    Microwave radiometer measurements of the Planck emission from soil can be used to estimate the near-surface soil moisture. A more straightforward and consistent model of this emission results if nonuniform, rather than uniform, plane waves are used. Adaptation of this new model to a layered medium representation for the soil is improved using a normalization that is based on the isothermal soil limit. The separate concepts of radiometer sensing depth and in situ sampling depth for soil moisture are examined and theory for the sensing depth is presented. Improved approximations (at 1413 MHz) to the full model for soil emission are developed since they are needed to construct algorithms that retrieve an estimate of soil moisture from the radiometer raw measurement. The error associated with the comparison of these remotely sense values against in situ measurements is calculated. Results suggest that this comparison error could be lowered if the soil moisture sampling depth was reduced to values less than 0.01 m from the currently used values near 0.02 m, although the effect of surface roughness has not yet been analyzed. View full abstract»

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  • Backscattering From Trees Explained by Random Propagation Times

    Page(s): 4000 - 4005
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    Dealing with radar backscattering from trees, the Wong model is a mixing of Gaussian spectra with parameters deduced from considerations on motions of branches and leaves. Very detailed experiments by Narayanan et al. show gaps with this model. We show that autocorrelation functions by Narayanan et al are very well fitted by functions in the form exp[-|τ/τ0|α], 0 <; α ≤ 2. In this paper, we prove that the random propagation time theory explains this property. I have shown in other papers that this theory is available to study power spectra in acoustics, ultrasonics, and electromagnetics. View full abstract»

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  • Electromagnetic Radiation Fields in Three-Layered Media With Rough Interfaces

    Page(s): 4006 - 4013
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (440 KB) |  | HTML iconHTML  

    Research related to the radiation and propagation of the electromagnetic fields in half space or stratified media is of interest and is considered by many authors. In this paper, a theoretical study is discussed about the propagation of radio waves in the sea (three-layered media). The variations which occur in the shape of the sea surface and sea bottom are considered. The effects of the roughness exercised onto the electromagnetic field of arrangements radiating a pure transverse electric field in the sea are studied by using the perturbation method. Closed-form expression for the far field generated by a vertical magnetic dipole embedded below the sea surface is calculated by using a simple technique to evaluate Sommerfeld integrals with the aid of the complex image theory, which was quite difficult to evaluate previously. The results obtained are compared with those mentioned elsewhere. View full abstract»

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  • Asymmetry and Anisotropy of Microwave Backscatter at Low Incidence Angles

    Page(s): 4014 - 4024
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1311 KB) |  | HTML iconHTML  

    This paper presents the first results from a study of upwind/downwind asymmetry (UDA) and upwind/crosswind anisotropy (UCA) of the Ku-band radar backscatter at low incidence angles (0 ° to 18°) from satellite observations. Incidence angle, wind speed, and sea state dependence were a particular focus. Data were obtained from the Ku-band HH-polarized precipitation radar data in the Tropical Rainfall Mapping Mission colocated with measurements from buoys. It is shown that there is a statistically significant directional signal in the UDA and UCA data when the radar incidence angle is greater than 5°. In particular, a higher radar backscatter was observed from the downwind as opposed to the upwind look direction. This is contrary to scatterometer measurements at moderate incidence angles. As a whole, the main trends for these negative asymmetries increase the absolute magnitudes with increasing wind speed. This UDA is explained by the use of non-Gaussian statistics of the sea surface slope. By contrast, there is a change in behavior of the UCA of microwave backscatter at a critical wind speed of about 6 to 8 m/s. The magnitude of UCA apparently decreases with increasing wind speed below this wind speed range, and then it increases for higher wind speeds. Sensitivities to both incidence angle and sea state are also analyzed. View full abstract»

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  • Electromagnetic Scattering of Randomly Rough Soil Surfaces Based on Numerical Solutions of Maxwell Equations in Three-Dimensional Simulations Using a Hybrid UV/PBTG/SMCG Method

    Page(s): 4025 - 4035
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (910 KB) |  | HTML iconHTML  

    A hybrid UV/PBTG/SMCG method is developed to accelerate the solution of NMM3D for 3-D electromagnetic wave scattering by random rough soil surfaces. It takes only 1.5 min using 16 processors on a cluster of NSF TeraGrid to compute 3-D solution of Maxwell equations of 8 by 8 square wavelengths. With the improved computational efficiency, we computed results using areas up to 32 by 32 square wavelengths. With the larger surface area, we were able to compute cases with larger rms heights up to 8 cm at SMAP radar frequency of 1.26 GHz that corresponds to kh = 2.28. New results computed include the cross-polarization. The five tests on the accuracy of results were performed: convergence with realizations, convergence with discrete samplings, convergence with sample surface sizes, energy conservation for each realization, and reciprocity for each realization. The numerical results show that for a single realization VH = HV within 0.5 dB in the backscattering direction showing that reciprocity is obeyed. Results were compared with experimental data, and both co-polarization and cross-polarization were in good agreements with experimental data. View full abstract»

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  • Multi-Index Multi-Object Content-Based Retrieval

    Page(s): 4036 - 4049
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1440 KB) |  | HTML iconHTML  

    In many large-scale content-based retrieval (CBR) applications, the input to the search process is a complex query that may be composed of several constituent parts. The proposed approach performs CBR queries by breaking down a complex query into several smaller heterogeneous queries. Object-based queries in an imagery search application can be performed by executing a search over several distinct feature space indexes. For example, CBR indexes may exist for spectral, texture, and shape feature vectors extracted from objects. A query for similar objects can be completed by aggregating the results from these multiple indexes. Complementing this concept, a multi-object search can be used to identify relevant groups of objects which match a given set of query objects. For example, a set of objects identified in satellite imagery could be used as a CBR query in order to identify similar groups of objects. Thus, a query can be performed for each object, and these results can be aggregated into multi-object search results by determining the optimal match of the query objects to those in each resulting group. We introduce the absence penalty method and obligatory object query algorithms for performing multi-index and multi-object CBR searches and provide experimental results that show that the proposed approaches efficiently provide search results with a high degree of precision with minimal error. The experimental results shown demonstrate the efficiency and accuracy of the proposed methods; moreover, through the fusion of multi-index and multi-object search techniques, we are able to construct new sophisticated query mechanisms. View full abstract»

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  • Robust Automatic Registration of Multimodal Satellite Images Using CCRE With Partial Volume Interpolation

    Page(s): 4050 - 4061
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (760 KB) |  | HTML iconHTML  

    One of the most important steps in data fusion is image registration. Automatic image-to-image registration for images captured by different sensors traditionally requires the use of information-theoretic similarity measures such as mutual information. Recently, a new similarity measure known as cross-cumulative residual entropy (CCRE) has been proposed for multimodal image registration in medical imaging applications. In this paper, we investigate the use of CCRE for multisensor registration of remote sensing imagery. In particular, we investigate the extreme case of registering synthetic aperture radar images to optical images. We also propose a novel extension to the Parzen-window optimization approach proposed by Thévenaz which involves applying partial volume interpolation in the calculation of the gradients of the similarity measure. Our experimental results show that our proposed approach which uses CCRE as the similarity measure and partial volume interpolation in the optimization procedure provides superior performance to other approaches investigated. View full abstract»

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  • A Scale-Synthesis Method for High Spatial Resolution Remote Sensing Image Segmentation

    Page(s): 4062 - 4070
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (959 KB) |  | HTML iconHTML  

    Multiscale segmentation is always needed to extract semantic meaningful objects for object-based remote sensing image analysis. Choosing the appropriate segmentation scales for distinct ground objects and intelligently combining them together are two crucial issues to get the appropriate segmentation result for target applications. With respect to these two issues, this paper proposes a simple scale-synthesis method which is highly flexible to be adjusted to meet the segmentation requirements of varying image-analysis tasks. The main idea of this method is to first divide the whole image area into multiple regions; each region consisted of ground objects that have similar optimal segmentation scale. Then, synthesize the suboptimal segmentations of each region to get the final segmentation result. The result is the combination of suboptimal scales of objects and is therefore more coherent to ground objects. To validate this method, the land-cover-category map is used to guide the scale synthesis of multiscale image segmentations for the Quickbird-image land-use classification. First, the image is coarsely divided into multiple regions; each region belongs to a certain land-cover category. Then, multiscale-segmentation results are generated by the Mumford-Shah function based region-merging method. For each land-cover category, the optimal segmentation scale is selected by the supervised segmentation-accuracy-assessment method. Finally, the optimal scales of segmentation results are synthesized under the guide of land-cover category. It is proved that the proposed scale-synthesis method can generate a more accurate segmentation result that benefits the latter classification. The land-use-classification accuracy reaches to 77.8%. View full abstract»

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  • Active Learning Methods for Biophysical Parameter Estimation

    Page(s): 4071 - 4084
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1431 KB) |  | HTML iconHTML  

    In this paper, we face the problem of collecting training samples for regression problems under an active learning perspective. In particular, we propose various active learning strategies specifically developed for regression approaches based on Gaussian processes (GPs) and support vector machines (SVMs). For GP regression, the first two strategies are based on the idea of adding samples that are dissimilar from the current training samples in terms of covariance measure, while the third one uses a pool of regressors in order to select the samples with the greater disagreements between the different regressors. Finally, the last strategy exploits an intrinsic GP regression outcome to pick up the most difficult and hence interesting samples to label. For SVM regression, the method based on the pool of regressors and two additional strategies based on the selection of the samples distant from the current support vectors in the kernel-induced feature space are proposed. The experimental results obtained on simulated and real data sets show that the proposed strategies exhibit a good capability to select samples that are significant for the regression process, thus opening the way to the active learning approach for remote-sensing regression problems. View full abstract»

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  • Artificial DNA Computing-Based Spectral Encoding and Matching Algorithm for Hyperspectral Remote Sensing Data

    Page(s): 4085 - 4104
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2479 KB) |  | HTML iconHTML  

    In this paper, a spectral encoding and matching algorithm inspired by biological deoxyribonucleic acid (DNA) computing is proposed to perform the task of spectral signature classification for hyperspectral remote sensing data. As a novel branch of computational intelligence, DNA computing has the strong computing and matching capability to discriminate the tiny differences in DNA strands by DNA encoding and matching in the molecule layer. Similar to DNA discrimination, a hyperspectral remote sensing data matching approach is used to recognize the land cover material from a spectral library or image, according to the rich spectral information. However, it is difficult to apply DNA computing to hyperspectral remote sensing data processing because traditional DNA computing often relies on biochemical reactions of DNA molecules and may result in incorrect or undesirable computations. To utilize the advantages and avoid the problems of biological DNA computing, an artificial DNA computing approach is proposed for spectral encoding and matching for hyperspectral remote sensing data. A DNA computing-based spectral matching approach is used to first transform spectral signatures into DNA codewords by capturing the key spectral features with a spectral feature encoding operation. After DNA encoding, the typical DNA database for interesting classes is constructed and saved by DNA evolutionary operating mechanisms such as crossover, mutation, and structured mutation. During the course of spectral matching, each pixel of the hyperspectral image, or each signature measured in the field, is input to the constructed DNA database. By computing the distance between an unclassified spectrum and the typical DNA codewords from the database, the class property of each pixel is set as the minimum distance class. Experiments using different hyperspectral data sets were performed to evaluate the performance of the proposed artificial DNA computing-based spectral matching algorithm by comp- ring it with other traditional hyperspectral classifiers, including spectral matching classifiers (binary coding, spectral angle mapper and spectral derivative feature coding (SDFC) matching methods) and a novel statistical method of machine learning termed support vector machine (SVM). Experimental results demonstrate that the proposed algorithm is distinctly superior to the three traditional hyperspectral data classification algorithms. It presents excellent processing efficiency, compared to SVM, with high-dimensional data captured by the Hyperspectral Digital Imagery Collection Experiment sensor, and hence provides an effective option for spectral matching classification of hyperspectral remote sensing data. View full abstract»

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  • A Fast and Automatic Sparse Deconvolution in the Presence of Outliers

    Page(s): 4105 - 4116
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1035 KB) |  | HTML iconHTML  

    We present an efficient deconvolution method to retrieve sparse reflectivity series from seismic data in the presence of additive Gaussian and non-Gaussian noise. The problem is first formulated as an unconstrained optimization including a mixed lp - l1 measure for the data misfit and for the model regularization term, respectively. An efficient algorithm based on the alternating split Bregman technique is developed, and a numerical procedure based on the generalized cross-validation (GCV) technique is presented for the selection of the corresponding regularization parameter. To circumvent excessive computations of multiple optimizations to determine the minimizer of GCV curve, we formulate the deconvolution problem in the frequency domain as a basis pursuit denoising and solve it using the split Bregman algorithm with computational complexity O(Nlog(N)). Apart from significant stability against outliers in the data, the main advantage of such formulation is that the GCV curve can be generated during the iterations of the optimization procedure. The minimizer of the GCV curve is then used to properly determine the error bound in the data and hence the optimum number of iterations. Numerical experiments show that the proposed method automatically generates high-resolution solutions by only a few iterations needless of any prior knowledge about the noise in the data. View full abstract»

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  • Measurement of Sea Surface Wind Direction Using Bistatic High-Frequency Radar

    Page(s): 4117 - 4122
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (536 KB) |  | HTML iconHTML  

    A method for extracting sea surface wind direction information from bistatic high-frequency (HF) radar Doppler spectra is presented. By analogy to the monostatic case, the ratio of the intensities of the positive and negative bistatic Bragg peaks is used to derive the (ambiguous) wind direction. For bistatic operation, the reference is taken with respect to the scattering ellipse normal rather than the radar beam direction. The method is shown to be valid based on simulated bistatic HF radar Doppler spectra. Wind direction is also extracted from the bistatic radar data collected on the Southern China coast. Comparison between the radar-measured wind directions and those obtained from the Advanced Scatterometer shows good agreement. View full abstract»

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  • A Portable Real-Time Digital Noise Radar System for Through-the-Wall Imaging

    Page(s): 4123 - 4134
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1745 KB) |  | HTML iconHTML  

    We present the design and implementation of a portable, digital, real-time random noise radar system operating in the ultrahigh frequency range for through-the-wall detection and imaging. Noise radar technology is combined with modern digital signal processing approaches to architect a system to covertly perform range imaging of obscured stationary and moving targets as well as to detect the presence of humans via micro-Doppler detection combined with empirical mode decomposition. We model the propagation and sampling nonidealities in the system and propose techniques to overcome the effect of these nonidealities. Experimental results demonstrate the system's capability to image target scenes and characterize human activity from different stand-off distances. View full abstract»

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  • Extraction of Landmine Features Using a Forward-Looking Ground-Penetrating Radar With MIMO Array

    Page(s): 4135 - 4144
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1374 KB) |  | HTML iconHTML  

    A vehicle-mounted forward-looking ground-penetrating radar (GPR) with multiple-input and multiple-output (MIMO) array can obtain the high-resolution image of its front area to perform the standoff detection of landmines. The major challenge for the GPR landmine detection over wide areas is the very high false alarm rate when maintaining a high detection probability. In this paper, a novel feature extraction method is proposed to obtain the bistatic scattering information from the MIMO array image to discriminate landmines from clutter. To realize the goal, an imaging model of the MIMO array is firstly developed. Based on the imaging model, the bistatic scattering function of a suspected object is estimated from its MIMO array image using the space-wavenumber processing. Images of different incident angles and bistatic angles at some resonance frequencies are selected from the estimated bistatic scattering function to represent the scattering characteristics. In order to obtain the scale, rotation, and translation invariant feature vector, Hu moment invariants of the selected images are calculated to form the low-dimensional feature vector. The experimental results show that the proposed method can offer an efficient feature vector for the landmine discriminator to improve the detection performance. View full abstract»

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  • SAR-Based Vibration Estimation Using the Discrete Fractional Fourier Transform

    Page(s): 4145 - 4156
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1500 KB) |  | HTML iconHTML  

    A vibration estimation method for synthetic aperture radar (SAR) is presented based on a novel application of the discrete fractional Fourier transform (DFRFT). Small vibrations of ground targets introduce phase modulation in the SAR returned signals. With standard preprocessing of the returned signals, followed by the application of the DFRFT, the time-varying accelerations, frequencies, and displacements associated with vibrating objects can be extracted by successively estimating the quasi-instantaneous chirp rate in the phase-modulated signal in each subaperture. The performance of the proposed method is investigated quantitatively, and the measurable vibration frequencies and displacements are determined. Simulation results show that the proposed method can successfully estimate a two-component vibration at practical signal-to-noise levels. Two airborne experiments were also conducted using the Lynx SAR system in conjunction with vibrating ground test targets. The experiments demonstrated the correct estimation of a 1-Hz vibration with an amplitude of 1.5 cm and a 5-Hz vibration with an amplitude of 1.5 mm. View full abstract»

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  • Echo Separation in Multidimensional Waveform Encoding SAR Remote Sensing Using an Advanced Null-Steering Beamformer

    Page(s): 4157 - 4172
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2267 KB) |  | HTML iconHTML  

    In order to reap the potential benefits that waveform diversity can provide for spaceborne synthetic aperture radar remote sensing, echoes from different subpulses constituting a complete transmit waveform should be effectively separated at first. This paper presents a new separation approach implemented by an advanced null-steering beamformer on satellite. Compared with common null-steering beamforming, our approach will take into account the characteristics of echo signal from the scene and accordingly embed the finite-impulse response (FIR) filtering process into the modified null-steering beamformer to deal with the issue of pulse extension. In this paper, echo signals generated by multidimensional encoded waveform will be analyzed in detail; based on this analysis, FIR filter and the new null-steering beamformer are derived. Simulation results show that much better separation performance can be obtained by our approach than by conventional null-steering beamforming. View full abstract»

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Aims & Scope

 

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.

 

Full Aims & Scope

Meet Our Editors

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