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Geoscience and Remote Sensing, IEEE Transactions on

Issue 4  Part 2 • Date April 2013

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Displaying Results 1 - 25 of 33
  • Front cover

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

    Page(s): C2
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  • Table of contents

    Page(s): 2153 - 2154
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  • Evaluation of Different Methods to Retrieve the Hemispherical Downwelling Irradiance in the Thermal Infrared Region for Field Measurements

    Page(s): 2155 - 2165
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (501 KB) |  | HTML iconHTML  

    The thermal infrared hemispherical downwelling irradiance (HDI) emitted by the atmosphere and surrounding elements contributes through reflection to the signal measured over an observed surface by remote sensing. This irradiance must be estimated in order to obtain accurate values of land-surface temperature (LST). There are some fast methods to measure the HDI with a single measurement pointing to the sky at a specified viewing direction, but these methods require completely cloud-free or cloudy skies, and they do not account for the radiative contribution of surrounding elements. Another method is the use of a diffuse reflectance panel (usually, a rough gold-coated surface) with near-Lambertian behavior. This method considers the radiative contribution of surrounding elements and can be used under any sky condition. A third possibility is the use of atmospheric profiles and a radiative transfer code (RTC) in order to simulate the atmospheric signal and to calculate the HDI by integration. This study compares the HDI estimations with these approaches, using measurements made on four different days with a completely clear sky and two days with a partially cloudy sky. The measurements were made with a four-channel CIMEL Electronique radiometer working in the 8–14-$muhbox{m}$ spectral range. The HDI was also estimated by means of National Centers for Environmental Prediction atmospheric profiles introduced in the MODTRAN RTC. Additionally, the measurements were made at two different places with very different environments to quantify the effect of the contributing surroundings. Results showed that, for a clear-sky day with a minimal contribution of the surroundings, all methods differed from each other between 5% and 11%, depending on the spectral range, and any of them could be used to estimate HDI in these conditions. However, in the case of making surface measurements in an area with signi- icant surrounding elements (buildings, trees, etc.), HDI values retrieved from the panel present an increase of $+3 hbox{W}cdothbox{m}^{-2}cdotmuhbox{m}^{-1}$ compared with the other methods; this increase, if ignored, implies to make an error in LST ranging from $+0.5 ^{circ}hbox{C}$ to $+1.5 ^{circ}hbox{C}$, depending on the spectral range and on surface emissivity and temperature. Comparison under heterogeneous skies with changing cloud coverage showed also large differences between the use of panel and the other methods, reaching a maximum difference of $+4.6 hbox{W}cdothbox{m}^{-2}cdotmuhbox{m}^{-1}$, which implies to make an error on LST of $+2.2 ^{circ}hbox{C}$. In these cases, the use of the diffuse reflectance panel is proposed, since it is the unique way to capture the contribution of the surroundings and also to adequately measure HDI for sky changing conditions. View full abstract»

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  • Description and Validation of an Automated Objective Technique for Identification and Characterization of the Integrated Water Vapor Signature of Atmospheric Rivers

    Page(s): 2166 - 2176
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    An automated, objective tool for identifying and characterizing the integrated water vapor (IWV) signature of atmospheric rivers (ARs) based on satellite-observed or model-derived IWV fields has been developed, demonstrated, and validated. ARs are narrow plumes of intense water vapor transport that have been found to be an important contributor to major flooding events in the western U.S. and to seasonal water supply. Previous results demonstrated that the associated IWV signature is an effective proxy for the ARs themselves and that signature is used in this work to characterize the features. The technique employs basic objective criteria for the length ( $>$ 2000 km), width ($<$ 1000 km), and IWV content ($>$ 2 cm) of the plumes and standard image processing techniques including thresholding and skeletonization to identify the ARs. Extracted characteristics for the identified plumes include their position, width, core IWV content, orientation, lifetime, and propagation speed. The performance of the AR detection tool (ARDT) was validated over five cool seasons by comparing the AR IWV signature identified by the tool with visually identified events from an existing landfalling AR climatology. The ARDT performed extremely well with a critical success index of 92.4% and a 98.5% probability of detection. Differences were largely the result of subjective decisions in visual classification and tradeoffs in the tool sensitivity between missing actual ARs and inclusion of non-AR features. Future improvements include refined computations of the length and width of AR features and extension of the technique to apply directly to measurements of the water vapor transport. Overall, the ARDT appears well suited for the development of extended AR climatologies and- the comparison and verification of forecasts of ARs. View full abstract»

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  • Searchlight: Precipitation Advection Tracking Using Multiplatform Low-Earth-Orbiting Satellite Data

    Page(s): 2177 - 2187
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    This paper presents a new algorithm, Searchlight, capable of tracking precipitation advection through the complex spatiotemporal sampling pattern of multiplatform low-Earth-orbiting (LEO) passive and active microwave satellite data sets. The algorithm is based on the concept of “shining” a searchlight forward in time along proposed advection velocity vectors from a given LEO overpass. The searchlight “beam” links precipitation patterns in the current satellite overpass to corresponding patterns in multiple future overpasses. A global optimization determines an advection model by maximizing mutual information between linked precipitation measurements. The algorithm has been validated against surface radar over a region covering the Central and Eastern U.S. View full abstract»

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  • Validating ICESat Over Thick Sea Ice in the Northern Canada Basin

    Page(s): 2188 - 2200
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1851 KB) |  | HTML iconHTML  

    Only in the past eight years has the feasibility of using satellite-borne altimeters to estimate sea ice freeboard and thickness been demonstrated, and these estimates still have uncertainties primarily associated with limited knowledge of snow loading on sea ice. Because accurate estimates of Arctic-wide sea ice thickness and volume are fundamental inputs to global climate models, validation of satellite-derived thickness estimates using independent data is required. A detailed assessment of freeboard retrieved by the Geoscience Laser Altimeter System (GLAS) aboard the Ice, Cloud, and land Elevation Satellite has been carried out using high-resolution laser altimetry from the National Aeronautics and Space Administration's Airborne Topographic Mapper (ATM), the Delay-Doppler radar altimeter, and digital photography collected along a 300-km segment of sea ice in the Canada Basin. Exploiting the repeat coverage of the aircraft flight line, a correction was applied to GLAS footprint geolocations to adjust for sea ice drift that occurred during the time between satellite and aircraft acquisitions. Comparisons of GLAS and ATM measurements over sea ice show excellent agreement (about a 0.00-m mean) with no apparent bias between data sets. Freeboard estimates were examined using data from GLAS and ATM independently, employing measurements over refrozen leads to estimate local sea surface heights (SSHs). The results demonstrate the sensitivity of freeboard and thickness calculations to an accurate estimation of local SSH. Snow depth derived by differencing laser and radar data was combined with the freeboard estimates to yield a mean sea ice thickness of $sim$5.5 m over a 250-km subsection of the flight track. View full abstract»

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  • Soil Moisture Estimation Under Low Vegetation Cover Using a Multi-Angular Polarimetric Decomposition

    Page(s): 2201 - 2215
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    The estimation of volumetric soil moisture under low agricultural vegetation from fully polarimetric synthetic aperture radar (SAR) data at L-band using a multi-angular polarimetric decomposition is investigated. Radar polarimetry provides the framework to decompose the backscattered signal into different canonical scattering mechanisms referring to scattering contributions from the underlying soil and the vegetation cover. Multi-angular observation diversity further increases the information space for soil moisture inversion enabling higher inversion rates and a stable inversion performance. The developed approach was applied on the multi-angular L-band data set acquired by German Aerospace Center's ESAR sensor as part of the OPAQUE campaign in 2008. The obtained results are compared against ground measurements collected by the OPAQUE team over a variety of vegetated agricultural fields. The validation of the estimated against ground measured soil moisture results in an root mean square error level of 6–8 vol.$%$ including all test fields with a variety of crop types. View full abstract»

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  • Growth-Competition-Based Stem Diameter and Volume Modeling for Tree-Level Forest Inventory Using Airborne LiDAR Data

    Page(s): 2216 - 2226
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1044 KB) |  | HTML iconHTML  

    An individual tree within a forest stand will have its height and diameter growth restricted by the influence of neighboring trees. This is because trees in close proximity compete for resources and space to enable growth. In this paper, the position of trees, tree height (LH), tree crown radius (LCR), and growth competition index (LCI) were extracted from a light-detection-and-ranging (LiDAR)-based rasterized canopy height model using the multilevel morphological active-contour algorithm. The diameter and volume of individual trees are tested and validated to be an exponential function of those LiDAR-derived tree parameters. The best LiDAR-based diameter estimation model and volume estimation model were tested as significant with an $R^{2}$ value of 0.84 and 0.9 and evaluated with an estimation bias of 8.7 cm and 0.91 $ hbox{m}^{3}$, respectively. Results also showed that LH and LCR are positively related to the LiDAR-derived diameter at breast height (DBH) and the LiDAR-derived volume of individual trees in a forest stand, whereas LCI is negatively related. The proposed algorithm of individual tree volume estimation was further applied to predict the volume of three sample plots in mountainous forest stands. It was found that the LVM could be used to predict an acceptable volume estimate of old-aged forest stands. The estimation bias, i.e., percentage RMSE (RMSE%), is averaged at around 4% using the LiDAR metrics $lnhbox{LH}$, LCI, and LCR, whereas the RMSE% increases to 50% if only $lnhbox{LH}$ is applied. Results suggest that LCI is an important regulation factor in the estimation of forest volume stocks using LiDAR remote sensing. View full abstract»

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  • Multiyear Crop Monitoring Using Polarimetric RADARSAT-2 Data

    Page(s): 2227 - 2240
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    This paper studies the feasibility of monitoring crop growth based on a trend analysis of three elementary radar scattering mechanisms using three consecutive years (2008–2010) of RADARSAT-2 (R-2) Fine Quad Mode data. The polarimetric synthetic aperture radar analysis is based on the Pauli decomposition. Multitemporal analysis is applied to RGB images constructed using surface scattering, double-bounce, and volume scattering, along with intensity analysis of these scattering mechanisms. The test site is located in Eastern Ontario, Canada where the cropping system is dominated by corn, spring wheat, and soybeans. Each crop has unique physical structural characteristics which provide different responses for these scattering mechanisms. Significant changes occur in these scattering mechanisms as the crops move from one phenological stage to the next. By monitoring these changes over the season, the crop growth cycle from emergence to harvest can be observed. When harvest occurs, the backscatter intensities change significantly, and these changes aid in identifying crops. The temporal evaluation of the intensity of the scattering mechanisms generally track the measured leaf area index and observed phenological plant development. Changes in growth stage are crop type specific. Thus, to monitor changes in crop phenology and the occurrence of harvest activities, knowledge of the crop grown in any particular field is required. To accommodate this requirement, a maximum likelihood classification was performed on the R-2 data to produce a crop map. An overall classification accuracy of 85$%$ was achieved. View full abstract»

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  • Three-Dimensional Image Reconstruction of Targets Under the Illumination of Terahertz Gaussian Beam—Theory and Experiment

    Page(s): 2241 - 2249
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (799 KB) |  | HTML iconHTML  

    In this paper, the wave equation based on phase shift migration technique is extended for terahertz 3-D imaging with quasi-optical transceivers. An analytical expression of the reconstructed 3-D point-spread function for targets under the illumination of a terahertz Gaussian beam was derived with this reconstruction technique. The quantitative relationship between the imaging quality and the parameters of the transmitted Gaussian beam was obtained, which provides a good criterion to be followed when designing the terahertz quasi-optical transceivers in the imaging systems. Moreover, the spatial sampling criterion was derived strictly which is also quantitatively related to the parameters of the transmitted Gaussian beam. Simulation results with fairly good agreement were given to verify the theoretical results derived in this paper. Finally, a monostatic prototype imager with a Gaussian beam transceiver was designed for the proof-of-principle experiments in 0.2-THz band. The 3-D imaging results of different targets and a mannequin with concealed threat objects were given to demonstrate the theoretical results obtained in this paper and the effectiveness of the 3-D terahertz image reconstruction for security applications. View full abstract»

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  • Fusion of MODIS Images Using Kriging With External Drift

    Page(s): 2250 - 2259
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    The Moderate Resolution Imaging Spectroradiometer (MODIS) has been used in several remote sensing studies, including land, ocean, and atmospheric applications. The advantages of this sensor are its high spectral resolution, with 36 spectral bands; its high revisiting frequency; and its public domain availability. The first seven bands of MODIS are in the visible, near-infrared, and mid-infrared spectral regions of the electromagnetic spectrum which are sensitive to spectral changes due to deforestation, burned areas, and vegetation regrowth, among other land-use changes, making near-real-time forest monitoring a suitable application. However, the different spatial resolution of the spectral bands placed in these spectral regions imposes challenges to combine them in forest monitoring applications. In this paper, we present an algorithm based on geostatistics to downscale five 500-m MODIS pixel bands to match two 250-m pixel bands. We also discuss the advantages and limitations of this method in relation to existing downscaling algorithms. Our proposed method merges the data to the best spatial resolution and better retains the spectral information of the original data. View full abstract»

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  • Multiple-Spectral-Band CRFs for Denoising Junk Bands of Hyperspectral Imagery

    Page(s): 2260 - 2275
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3114 KB) |  | HTML iconHTML  

    Denoising of hyperspectral imagery in the domain of imaging spectroscopy by conditional random fields (CRFs) is addressed in this work. For denoising of hyperspectral imagery, the strong dependencies across spatial and spectral neighbors have been proved to be very useful. Many available hyperspectral image denoising algorithms adopt multidimensional tools to deal with the problems and thus naturally focus on the use of the spectral dependencies. However, few of them were specifically designed to use the spatial dependencies. In this paper, we propose a multiple-spectral-band CRF (MSB-CRF) to simultaneously model and use the spatial and spectral dependencies in a unified probabilistic framework. Furthermore, under the proposed MSB-CRF framework, we develop two hyperspectral image denoising algorithms, which, thanks to the incorporated spatial and spectral dependencies, can significantly remove the noise, while maintaining the important image details. The experiments are conducted in both simulated and real noisy conditions to test the proposed denoising algorithms, which are shown to outperform the popular denoising methods described in the previous literatures. View full abstract»

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  • Hyperspectral Image Classification Based on Structured Sparse Logistic Regression and Three-Dimensional Wavelet Texture Features

    Page(s): 2276 - 2291
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    Hyperspectral remote sensing imagery contains rich information on spectral and spatial distributions of distinct surface materials. Owing to its numerous and continuous spectral bands, hyperspectral data enable more accurate and reliable material classification than using panchromatic or multispectral imagery. However, high-dimensional spectral features and limited number of available training samples have caused some difficulties in the classification, such as overfitting in learning, noise sensitiveness, overloaded computation, and lack of meaningful physical interpretability. In this paper, we propose a hyperspectral feature extraction and pixel classification method based on structured sparse logistic regression and 3-D discrete wavelet transform (3D-DWT) texture features. The 3D-DWT decomposes a hyperspectral data cube at different scales, frequencies, and orientations, during which the hyperspectral data cube is considered as a whole tensor instead of adapting the data to a vector or matrix. This allows the capture of geometrical and statistical spectral–spatial structures. After the feature extraction step, sparse representation/modeling is applied for data analysis and processing via sparse regularized optimization, which selects a small subset of the original feature variables to model the data for regression and classification purpose. A linear structured sparse logistic regression model is proposed to simultaneously select the discriminant features from the pool of 3D-DWT texture features and learn the coefficients of the linear classifier, in which the prior knowledge about feature structure can be mapped into the various sparsity-inducing norms such as lasso, group, and sparse group lasso. Furthermore, to overcome the limitation of linear models, we extended the linear sparse model to nonlinear classification by partitioning the feature space into subspaces of linearly separable samples. The advantages of our methods are validated on the real h- perspectral remote sensing data sets. View full abstract»

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  • Combining Superresolution and Fusion Methods for Sharpening Misrsat-1 Data

    Page(s): 2292 - 2301
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2115 KB) |  | HTML iconHTML  

    This paper presents an efficient technique for sharpening of Misrsat-1 data using superresolution (SR) methods and fusion methods. Due to the difference in spectral characteristics between bands 1 and 3 and the panchromatic (PAN) band of Misrsat-1, we implement SR on high details of these bands and use the resulting image to sharpen the bands of the multispectral (MS) image. Several SR methods are tested and compared in this paper for this purpose. The first class of methods uses spatial-domain SR, in which SR is performed on the high-pass details extracted from bands 1 and 3 and the PAN band. The superresolved high-pass details are used after that to enhance the spatial resolution of the MS data using the high-pass filter fusion method. The second class of methods depends on the interpolation of coefficients in the high-frequency subbands of a multiscale representation of bands 1 and 3 and the PAN band and an additive fusion method to add the high-frequency subband coefficients to different bands of the MS image. A comparison study between different SR methods belonging to the aforementioned classes such as nonuniform interpolation (NUI), projection onto convex sets (POCS), iterative back projection (IBP), structure-adaptive normalized convolution (SANC), and adaptive steering kernel regression (ASKR) is presented. The simulation results show that iterative SR methods such as IBP and POCS produce more noise than interpolation methods such as NUI, SANC, and ASKR. The results also reveal that combining the ASKR with a multiscale decomposition enhances the signal-to-noise ratio. View full abstract»

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  • Comparative Study of Some Population-Based Optimization Algorithms on Inverse Scattering of a Two-Dimensional Perfectly Conducting Cylinder in Dielectric Slab Medium

    Page(s): 2302 - 2315
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    The application of four techniques for the shape reconstruction of a 2-D metallic cylinder buried in dielectric slab medium by measured the scattered fields outside is studied in the paper. The finite-difference time-domain (FDTD) technique is employed for electromagnetic analyses for both the forward and inverse scattering problems, while the shape reconstruction problem is transformed into optimization one during the course of inverse scattering. Then, four techniques including asynchronous particle swarm optimization (APSO), PSO, dynamic differential evolution (DDE) and self-adaptive DDE (SADDE) are applied to reconstruct the location and shape of the 2-D metallic cylinder for comparative purposes. The statistical performances of these algorithms are compared. The results show that SADDE outperforms PSO, APSO and DDE in terms of the ability of exploring the optima. However, these results are considered to be indicative and do not generally apply to all optimization problems in electromagnetics. View full abstract»

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  • Wideband Autocorrelation Radiometric Sensing of Microwave Travel Time in Snowpacks and Planetary Ice Layers

    Page(s): 2316 - 2326
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (646 KB) |  | HTML iconHTML  

    Wideband autocorrelation radiometry (wideband AR) offers a deterministic method of remotely sensing microwave travel time $tau_{s}$ in planetary surface layers that are quasi-transparent to microwaves. Combining $tau_{s}$ with an independent estimate of the layer's average microwave index of refraction $n_{s}$ yields a measure of layer thickness whose accuracy is primarily limited by the accuracy of $n_{s}$. The technique requires that four conditions be met: 1) The correlation time of the radiometric signal must be less than the time difference at the radiometer between an upwelling ray that traverses the quasi-transparent layer once and a multiply reflected ray that traverses the quasi-transparent layer three times; 2) interfaces at the top and bottom of the layer must be effectively specular at the frequency of the radiometer; 3) dielectric transitions at the top and bottom of the layer must be distinct; and 4) rays transiting the layer must not be significantly absorbed or scattered. The performance of wideband AR for sensing dry snowpacks is governed by the relationship between system bandwidth and minimum snowpack thicknesses that can be sensed, the microwave indices of refraction of snowpacks and their underlying media, and the integration time required to depress the autocorrelation noise floor below the autocorrelation signal. Findings of this paper are that microwave travel times within dry snowpacks over frozen or thawed soils, or over ice, could be deterministically measured for snowpack thicknesses between 10 cm and 2 m using wideband AR sensors having 10-GHz center frequencies, 1-GHz bandwidths, and 1-ms integration times. View full abstract»

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  • Radiometric and Spatial Resolution Constraints in Millimeter-Wave Close-Range Passive Screener Systems

    Page(s): 2327 - 2336
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1472 KB) |  | HTML iconHTML  

    This paper presents a comparative study of the radiometric sensitivity and spatial resolution of three near-field (NF) passive screener systems: real aperture, 1-D synthetic aperture (SA), and 2-D SA radiometers are compared. The analytical expressions for the radiometric resolution, the number of required antennas, and the number of pixels in the image are derived taking into account the distortion produced by the NF geometry at nonboresight directions where the distortion is dominant. Based on the theoretical results, a performance comparison among the studied systems is carried out to show the advantages and drawbacks when using the radiometers in a close-range screening application. Moreover, the screener performance in a close-range environment is discussed from the results obtained in the aforementioned comparison. View full abstract»

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  • Modeling and Prediction of Rainfall Using Radar Reflectivity Data: A Data-Mining Approach

    Page(s): 2337 - 2342
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    Rainfall affects local water quantity and quality. A data-mining approach is applied to predict rainfall in a watershed basin at Oxford, Iowa, based on radar reflectivity and tipping-bucket (TB) data. Five data-mining algorithms, neural network, random forest, classification and regression tree, support vector machine, and $k$-nearest neighbor, are employed to build prediction models. The algorithm offering the highest accuracy is selected for further study. Model I is the baseline model constructed from radar data covering Oxford. Model II predicts rainfall from radar and TB data collected at Oxford. Model III is constructed from the radar and TB data collected at South Amana (16 km west of Oxford) and Iowa City (25 km east of Oxford). The computation results indicate that the three models offer similar accuracy when predicting rainfall at current time. Model II performs better than the other two models when predicting rainfall at future time horizons. View full abstract»

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  • Polarimetric Temporal Analysis of Urban Environments With a Ground-Based SAR

    Page(s): 2343 - 2360
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4448 KB) |  | HTML iconHTML  

    Revisiting time constitutes a key constraint for continuous monitoring activities based on space- and airborne synthetic aperture radar (SAR) acquisitions. Conversely, the employment of terrestrial platforms overcomes this limitation and makes it possible to perform time-continuous observations of small space-scale phenomena. New research lines of SAR dealing with the backscattering evolution of different types of scenarios become hence possible through the analysis of ground-based SAR (gbSAR) data collections. The Remote Sensing Laboratory of the Universitat Politècnica de Catalunya drove a one-year measurements campaign in the village of Sallent, northeastern Spain, using its X-Band gbSAR sensor. The field experiment aimed at studying the subsidence phenomenon induced by the salt mining activity carried out in this area during the past decades. In this paper, the polarimetric behavior of an urban environment is investigated at different time scales. After a brief description of the test site and the measurement campaign, the analysis is focused on the stability on man-made structures at different time scales. PolSAR data monthly acquired from June 2006 to July 2007 are employed to stress the presence of nonstationary backscattering processes within the urban scene and the effect they have on differential phase information. Then, a filtering procedure aiming at reducing backscattering randomness in one-day and long-term data collections is then put forward. The improvements provided by the proposed technique are assessed using a new polarimetric descriptor, the time entropy. In the end, the importance of preserving the interferometric phase information from nonstationary backscattering contaminations using fully polarimetric data is discussed. View full abstract»

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  • Computationally Efficient RF Interference Suppression Method With Closed-Form Maximum Likelihood Estimator for HF Surface Wave Over-The-Horizon Radars

    Page(s): 2361 - 2372
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1958 KB) |  | HTML iconHTML  

    Among all types of unwanted signals in high-frequency (HF) surface wave (HFSW) over-the-horizon (OTH) radars, radio-frequency interference (RFI) is dominant since HF band is shared by many radio services. In observation data, there are two types of common RFI. The most common one is the conventional RFI which presents vertical stripe paralleling to range axis in range-Doppler spectrum (RDS) and has been exhaustively reported by previous papers. Meanwhile, a new type of RFI characterized by sloping stripes (called $ hbox{RFI}_{rm SS}$) in RDS is also frequently observed. This work concentrates on the new $hbox{RFI}_{rm SS}$ and establishes a unified model for the above two types of RFI. Based on this generalized model, a time-domain RFI suppression algorithm is proposed here. Benefiting from a closed-form approximate maximum likelihood estimator, the proposed algorithm exhibits excellent performance and is computationally efficient. Its operational performance is evaluated using the field data recorded by experimental HFSW OTH radar of Wuhan University. View full abstract»

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  • A New Approach to Detect Ground Clutter Mixed With Weather Signals

    Page(s): 2373 - 2387
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (1992 KB) |  | HTML iconHTML  

    Considering that the statistics of the phase and the power of weather signals in the spectral domain are different from those statistics for echoes from stationary objects, a spectrum clutter identification (SCI) algorithm has been developed to detect ground clutter using single polarization radars, but SCI can be extended for dual-pol radars. SCI examines both the power and phase in the spectral domain and uses a simple Bayesian classifier to combine four discriminants: spectral power distribution, spectral phase fluctuations, spatial texture of echo power, and spatial texture of spectrum width to make decisions as to the presence of clutter that can corrupt meteorological measurements. This work is focused on detecting ground clutter mixed with weather signals, even if the clutter power to signal power ratio is low. The performance of the SCI algorithm is shown by applying it to radar data collected by University of Oklahoma-Polarimetric Radar for Innovation in Meteorology and Engineering. View full abstract»

    Open Access
  • Bayesian Wavelet Shrinkage With Heterogeneity-Adaptive Threshold for SAR Image Despeckling Based on Generalized Gamma Distribution

    Page(s): 2388 - 2402
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2425 KB) |  | HTML iconHTML  

    Synthetic aperture radar (SAR) images are inherently affected by multiplicative speckle noise, which will degrade the human interpretation and computer-aided scene analysis. In this paper, we propose a novel Bayesian multiscale method for SAR image despeckling in the non-homomorphic framework. To address the multiplicative nature, we first make the speckle contribution additive by a linear decomposition. Then, in the stationary wavelet transform domain, a two-sided generalized Gamma distribution (G$Gamma$D) is introduced as a prior to capture the heavy-tailed nature of wavelet coefficients of the noise-free reflectivity. By exploiting this prior together with a Gaussian likelihood, an analytical wavelet shrinkage function is derived based on maximum a posteriori criteria, which further adopts heterogeneity-adaptive thresholding technique to achieve better estimates of noise-free wavelet coefficients. Moreover, a pilot-signal-assisted strategy is proposed to estimate the parameters of two-sided G $Gamma$D with the estimator based on second-kind cumulants. Finally, experimental results, carried out on the synthetic and actual SAR images, are given to demonstrate the validity of the proposed despeckling method. View full abstract»

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  • Efficient Detection and Imaging of Moving Targets in SAR Images Based on Chirp Scaling

    Page(s): 2403 - 2416
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    An innovative scheme is presented for moving target detection and high-resolution focusing that exploits a bank of chirp scaling algorithms (CSA), each one matched to a different along track target velocity component. The new scheme is thought for multichannel (MC) synthetic aperture radar systems, to provide a high-resolution focusing of the moving targets. Adequate target detection capability is ensured by integrating the aforementioned bank of CSA with a post-Doppler space–time adaptive processing clutter cancellation step. The presented scheme is very efficient from a computational point of view and is able to achieve sub-clutter visibility for the moving targets. The effectiveness of the proposed techniques is shown with reference to an emulated MC data set. View full abstract»

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  • A Change Detection Approach to Flood Mapping in Urban Areas Using TerraSAR-X

    Page(s): 2417 - 2430
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    Very high resolution synthetic aperture radar (SAR) sensors represent an alternative to aerial photography for delineating floods in built-up environments where flood risk is highest. However, even with currently available SAR image resolutions of 3 m and higher, signal returns from man-made structures hamper the accurate mapping of flooded areas. Enhanced image processing algorithms and a better exploitation of image archives are required to facilitate the use of microwave remote-sensing data for monitoring flood dynamics in urban areas. In this paper, a hybrid methodology combining backscatter thresholding, region growing, and change detection (CD) is introduced as an approach enabling the automated, objective, and reliable flood extent extraction from very high resolution urban SAR images. The method is based on the calibration of a statistical distribution of “open water” backscatter values from images of floods. Images acquired during dry conditions enable the identification of areas that are not “visible” to the sensor (i.e., regions affected by “shadow”) and that systematically behave as specular reflectors (e.g., smooth tarmac, permanent water bodies). CD with respect to a reference image thereby reduces overdetection of inundated areas. A case study of the July 2007 Severn River flood (UK) observed by airborne photography and the very high resolution SAR sensor on board TerraSAR-X highlights advantages and limitations of the method. Even though the proposed fully automated SAR-based flood-mapping technique overcomes some limitations of previous methods, further technological and methodological improvements are necessary for SAR-based flood detection in urban areas to match the mapping capability of high-quality aerial photography. 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