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Geoscience and Remote Sensing, IEEE Transactions on

Issue 7 • Date July 2008

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Displaying Results 1 - 25 of 31
  • [Front cover]

    Publication Year: 2008 , Page(s): C1
    Save to Project icon | Request Permissions | PDF file iconPDF (197 KB)  
    Freely Available from IEEE
  • IEEE Transactions on Geoscience and Remote Sensing publication information

    Publication Year: 2008 , Page(s): C2
    Save to Project icon | Request Permissions | PDF file iconPDF (42 KB)  
    Freely Available from IEEE
  • Table of contents

    Publication Year: 2008 , Page(s): 1877 - 1878
    Save to Project icon | Request Permissions | PDF file iconPDF (67 KB)  
    Freely Available from IEEE
  • Interference Cancellation for High-Frequency Surface Wave Radar

    Publication Year: 2008 , Page(s): 1879 - 1891
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1425 KB) |  | HTML iconHTML  

    The performance of high-frequency surface wave radar (HFSWR) is known to suffer from external environmental interference and noise, such as cochannel radio-frequency interference from other radiating source, ionospheric clutter, lightning impulsive noise, etc. This paper experimentally evaluates the interference cancellation performance of various adaptive beamforming schemes with respect to the aforementioned three types of interferences in an attempt to find the most promising adaptive cancellation scheme in practical HFSWR environment. View full abstract»

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  • Classification of Ground Clutter and Anomalous Propagation Using Dual-Polarization Weather Radar

    Publication Year: 2008 , Page(s): 1892 - 1904
    Cited by:  Papers (14)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1448 KB) |  | HTML iconHTML  

    This paper presents the results of a study designed to classify weather radar clutter echoes obtained from ground-based dual-polarization weather radar systems. The clutter signals are due to ground clutter, sea clutter, and anomalous propagation echoes, which represent sources of error in quantitative radar rainfall estimation. Fuzzy and Bayes classifiers are evaluated as an alternative approach to traditional polarimetric-based methods. Both systems were trained and validated by using C-band dual- polarization radar measurements, and a novel technique is proposed to calculate the texture function to mitigate against the edge effects at the boundaries of precipitation regions. A methodology is presented to extract the membership functions and conditional probability density functions to train the classifiers. The critical success index indicates that the Bayes classifier has, on average, a slightly better performance than the fuzzy classifiers. However, when optimal weighting was applied, the fuzzy classifier gave one of the best performances. The classifiers are sufficiently robust to be used when only single-polarization radar measurements are available. View full abstract»

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  • Evaluation of SSM/I and AMSR-E Sea Ice Concentrations in the Antarctic Spring Using KOMPSAT-1 EOC Images

    Publication Year: 2008 , Page(s): 1905 - 1912
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (609 KB) |  | HTML iconHTML  

    To evaluate sea ice concentrations (SICs) from the special sensor microwave/imager (SSM/I) and advanced microwave scanning radiometer-EOS (AMSR-E), we observed sea ice with the 6-m-resolution panchromatic electronic optical camera (EOC) sensor onboard the Korea Multi-Purpose Satellite-1 (KOMPSAT-1). A total of 68 cloud-free EOC images were obtained across the Antarctic continental edges from September to November 2005. Sea ice types in the EOC images were classified into white ice (W), gray ice (G), and dark-gray ice (D) and then compared with SSM/I and AMSR-E SICs. Spatiotemporal standard deviation of passive microwave SIC proved useful in selecting temporally stable and spatially homogeneous SICs to overcome the diurnal variation of sea ice in the analysis of data from multiple satellites. In the Antarctic spring, the EOC SIC of W + G showed the best fit to SSM/I SIC calculated by the NASA Team (NT) algorithm (mean difference of -2.3% and rmse of 3.2%), whereas that of W + G + D showed the best fit to AMSR-E SIC calculated by the NT2 algorithm (mean difference of 0.3% and rmse of 1.4%). It is concluded that the SSM/I NT algorithm responds to young ice in addition to the ice types A and B, whereas the AMSR-E NT2 algorithm detects ice type C and thin ice as well. The 4.7% difference of SICs between AMSR-E and SSM/I was attributed to the enhanced detection of ice type C (2.1%) and thin ice (2.6%) of the AMSR-E NT2 algorithm. View full abstract»

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  • Comparison of ICESat Data With Airborne Laser Altimeter Measurements Over Arctic Sea Ice

    Publication Year: 2008 , Page(s): 1913 - 1924
    Cited by:  Papers (20)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1045 KB) |  | HTML iconHTML  

    Surface elevation and roughness measurements from NASA's Ice, Cloud, and land Elevation Satellite (ICESat) are compared with high-resolution airborne laser altimeter measurements over the Arctic sea ice north of Alaska, which were taken during the March 2006 EOS Aqua Advanced Microwave Scanning Radiometer sea ice validation campaign. The comparison of the elevation measurements shows that they agree quite well with correlations of around 0.9 for individual shots and a bias of less than 2 cm. The differences are found to decrease quite rapidly when applying running means. The comparison of the roughness measurements show that there are significant differences between the two data sets, with ICESat generally having higher values. The roughness values are only moderately correlated on an individual-shot basis, but applying running means to the data significantly improves the correlations to as high as 0.9. For the conversion of the elevation measurements into snow-ice freeboard, ocean surface elevation estimates are made with the high-resolution laser altimeter data, as well as several methods using lower resolution ICESat data. Under optimum conditions, i.e., when leads that are larger than the ICESat footprint are present, the ICESat- and Airborne Topographic Mapper-derived freeboards are found to agree to within 2 cm. For other areas, ICESat tends to underestimate the freeboard by up to 9 cm. View full abstract»

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  • Combining Artificial Neural Network Models, Geostatistics, and Passive Microwave Data for Snow Water Equivalent Retrieval and Mapping

    Publication Year: 2008 , Page(s): 1925 - 1939
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1328 KB) |  | HTML iconHTML  

    A new modeling framework combining neural-network-based models, passive microwave data, and geostatistics is proposed for snow water equivalent (SWE) retrieval and mapping. Brightness temperature data from the seven-channel special sensor microwave/imager and the interpolated minimum temperature are the inputs of a multilayer feedforward neural network (MFF). Kriging with an external drift algorithm is applied to ground-based SWE data to produce gridded SWE data that are used as the target of the neural network. An optimal division of the sample of available pixels is achieved by a self-organizing feature map. Prediction error is used for model selection and is assessed by bootstrap. It is shown that a committee of a network containing neural networks with different architectures can provide consistent SWE retrievals. This modeling framework is applied for SWE retrieval and mapping over La Grande River basin in north eastern Quebec (Canada). The results are very promising for operational purposes particularly for SWE mapping during periods with no ground measurements and operational streamflow forecasting. View full abstract»

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  • Uncertainties Associated With the Surface Texture of Ice Particles in Satellite-Based Retrieval of Cirrus Clouds—Part I: Single-Scattering Properties of Ice Crystals With Surface Roughness

    Publication Year: 2008 , Page(s): 1940 - 1947
    Cited by:  Papers (18)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (310 KB) |  | HTML iconHTML  

    Surface roughness of ice crystals is a morphological parameter important to the scattering characteristics of these particles. The intent of this paper, reported in two parts (hereafter, Parts I and II), is to investigate the accuracy associated with some simplifications in calculating the single-scattering properties of roughened ice crystals and to quantify the effect of surface roughness on the retrieval of the optical and microphysical properties of ice clouds from satellite observations. In Part I, two ray-tracing schemes, a rigorous algorithm and an approximate algorithm with a simplified treatment of surface roughness, are employed to calculate the single-scattering properties of randomly oriented hexagonal ice crystals with size parameters in the geometric optics regime. With the rigorous approach, it requires substantial computational effort to accurately account for the multiple external reflections between various roughness facets and the reentries of outgoing rays into the particles in the ray-tracing computation. With the simplified ray-tracing scheme, the ray-tracing calculation for roughened particles is similar to that for smooth particles except that, in the former case, the normal of the particle surface is statistically perturbed for each reflection-refraction event. The simplified ray-tracing scheme can account for most the effects of surface roughness on particle single-scattering properties without incurring substantial demand on computational resources and, thus, provides an efficient way to compute the single-scattering properties of roughened particles. The effect of ice-crystal surface roughness on the retrieval of the optical thicknesses and effective particle sizes of cirrus clouds is reported in Part II. View full abstract»

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  • Uncertainties Associated With the Surface Texture of Ice Particles in Satellite-Based Retrieval of Cirrus Clouds: Part II—Effect of Particle Surface Roughness on Retrieved Cloud Optical Thickness and Effective Particle Size

    Publication Year: 2008 , Page(s): 1948 - 1957
    Cited by:  Papers (18)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1052 KB) |  | HTML iconHTML  

    The simplified ray-tracing technique reported in Part I of this paper is employed to compute the single-scattering properties of hexagonal columns with maximum dimensions ranging from 2 to 3500 mum with a size-bin resolution of 2 mum at wavelengths of 0.86 and 2.13 mum. For small ice crystals, the current treatment of surface roughness may not be adequate because the applicability of the principles of geometric optics breaks down for small roughness scale. However, for ice crystals smaller than 40 mum, the aspect ratios of these particles are close to one, and the effect of surface roughness is quite small. In this paper, the diffraction is accounted for in the same way as in the case of smooth particles. It is essentially unfeasible to incorporate the effect of surface roughness into the numerical computation of the diffraction contribution. The scattering properties of individual ice crystals are then averaged over 18 particle size distributions whose effective particle radii (re) range from 5 to 90 mum. The single-scattering properties of ice clouds are strongly sensitive to surface roughness condition. Lookup tables that are built for the correlation between the bidirectional reflectances at wavelengths of 0.86 and 2.13 mum with different roughness conditions are used to retrieve ice cloud optical thickness and effective particle size over oceans. Pronounced differences are noticed for the retrieved cirrus cloud optical thickness and effective particle sizes in conjunction with different surface roughness conditions. The values of the retrieved cirrus cloud optical thickness in the case of the rough surface are generally smaller than their counterparts associated with smooth surface conditions. The effect of surface roughness on the retrieved effective particle radii is not pronounced for slight and moderate roughness conditions. However, when the surfaces of ice crystals are substantially rough, the retrieved effective radii associated with roughened- - particles are larger and smaller than their smooth surface counterparts forlarge (re>50 mum) and small (re<35 mum) ice crystals, respectively, whereas the effect of surface roughness on the retrieved effective radii shows a nonmonotonic feature for moderate particle sizes (35 mum<re<50 mum). In general, the dominant effect of surface roughness on cloud property retrievals is to decrease the retrieved optical thickness and to increase the retrieved effective particle size in comparison with their counterparts in the case of smooth ice particles. View full abstract»

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  • Considerations on Water Vapor and Surface Reflectance Retrievals for a Spaceborne Imaging Spectrometer

    Publication Year: 2008 , Page(s): 1958 - 1966
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (605 KB) |  | HTML iconHTML  

    The retrievals of atmospheric water vapor column and surface reflectance from air- or spaceborne hyperspectral imagery require accurate spectroradiometric calibration and a radiative transfer (RT) code. Since RT codes are too time consuming to be run on a per-pixel basis, a common technique employs the offline compilation of an atmospheric database and its subsequent use for the atmospheric correction of the image cube. The challenge is to design the size of the database as small as possible for a requested retrieval accuracy. We present a methodology to compile the database for a specified retrieval accuracy in water vapor and surface reflectance for a given set of input surface reflectance spectra and a chosen RT algorithm. The method is applied as a case study conducted for the planned German imaging spectrometer EnMAP. Some tradeoff considerations are also discussed. For the specified range of columnar water vapor (0.5-4.5 cm), results demonstrate that five water vapor grid points in the database are sufficient to achieve the requested relative root-mean-square retrieval accuracies of 2% and 3% in water vapor and surface reflectance, respectively. It should be pointed out that this is not intended as a general claim of retrieval accuracy achievable under typical remote sensing conditions, but these figures apply only to the theoretical conditions of the calculation, i.e., assuming the same conditions for forward simulation and retrieval. Nevertheless, these figures are indispensable for the design of a database, which is an important step for the atmospheric correction of imaging spectrometer data and the sole topic of this paper. View full abstract»

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  • Shape Reconstruction of a Perfectly Conducting Scatterer Using Differential Evolution and Particle Swarm Optimization

    Publication Year: 2008 , Page(s): 1967 - 1974
    Cited by:  Papers (43)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (324 KB) |  | HTML iconHTML  

    The shape reconstruction of a perfectly conducting 2-D scatterer by inverting transverse magnetic scattered field measurements is investigated. The reconstruction is based on evolutionary algorithms that minimize the discrepancy between measured and estimated scattered field data. A closed cubic B-spline expansion is adopted to represent the scatterer contour. Two algorithms have been examined the differential-evolution (DE) algorithm and the particle swarm optimization (PSO). Numerical results indicate that the DE algorithm outperforms the PSO in terms of reconstruction accuracy and convergence speed. Both techniques have been tested in the case of simulated measurements contaminated by additive white Gaussian noise. View full abstract»

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  • Sparsification of the Impedance Matrix in the Solution of the Integral Equation by Using the Maximally Orthogonalized Basis Functions

    Publication Year: 2008 , Page(s): 1975 - 1981
    Cited by:  Papers (8)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (394 KB) |  | HTML iconHTML  

    The higher order vector basis functions defined in large patches have been utilized in the numerical solution of integral equations in this paper to sparsify the impedance matrix and relieve the memory pressure. The physical explanation for the sparsification of the impedance matrix is also elucidated. Furthermore, the maximally orthogonalized bases have been applied to improve the condition number of the impedance matrix. The scaling factor was reformed to speed up the iteration convergence in the numerical solution. Finally, the iterative method for sparse matrix equations is applied to improve the solution efficiency. Some numerical results are provided to illustrate the excellent performance both in the sparsification of the impedance matrix and solution efficiency for numerical analysis of the scattering problem. View full abstract»

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  • Testing a New Model for the L-Band Radiation of Moist Leaf Litter

    Publication Year: 2008 , Page(s): 1982 - 1994
    Cited by:  Papers (12)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (759 KB) |  | HTML iconHTML  

    The crown vegetation of a deciduous forest is known to be semitransparent at low microwave frequencies, and leaf litter covering the forest soil has been recognized to have a significant impact on ground emission. The proposed approach for modeling the L-band radiative transfer through leaf litter consists of an isotropic effective medium approach for the litter permittivities, a coherent radiative transfer model for computing the coherent reflectivities from dielectric depth profiles, and an averaging procedure for computing the reflectivities determining the field-scale brightness temperatures. Evaluations were performed for the case of leaf litter on top of a conducting wire grid (litter-grid formation) and for litter on underlying soil (litter-soil formation). A model sensitivity analysis was performed with respect to parameters characterizing litter thickness variations and boundary roughness. For the litter-soil formation, the model was rather sensitive to local irregularities at the air-to-litter boundary. Modeled microwave signatures reproduced the major features of the measurements performed on a site comprising a litter-grid formation. Under dry conditions, the investigated litter layer was nearly ldquoinvisible.rdquo When the same litter layer was wetted, it acted as an important radiation source to be taken into account for the quantitative remote soil moisture detection of forested areas. Under certain conditions, the simulations revealed an increasing brightness when the litter is wetted prior to the underlying soil. Further wetting of the litter-soil system then resulted in a decreasing brightness as expected for increased moisture. Such effects are important to know to avoid misleading interpretations of L-band signatures. View full abstract»

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  • Hydrotope-Based Protocol to Determine Average Soil Moisture Over Large Areas for Satellite Calibration and Validation With Results From an Observation Campaign in the Volta Basin, West Africa

    Publication Year: 2008 , Page(s): 1995 - 2004
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (959 KB) |  | HTML iconHTML  

    In West Africa, which is an extremely moisture-limited region, soil water information plays a vital role in hydrologic and meteorologic modeling for improved water resource planning and food security. Recent and upcoming satellite missions, such as SMOS and MetOp, hold promise for the regional observation of soil moisture. The resolution of the satellites is relatively coarse (>100 km2), which brings with it the need for large-scale soil moisture information for calibration and validation purposes. We put forward a soil moisture sampling protocol based on hydrotopes. Hydrotopes are defined as landscape units that show internally consistent hydrologic behavior. This hydrotope analysis helps in the following ways: 1) by ensuring statistically reliable validation via the reduction of the overall pixel variance and 2) by improving sampling schemes for ground truthing by reducing the chance of sampling bias. As a sample application, we present data from three locations with different moisture regimes within the Volta Basin during both dry and wet periods. Results show that different levels of reduction in the overall pixel variance of soil moisture are obtained, depending on the general moisture status. With respect to the distinction between the different hydrotope units, it is shown that under intermediate moisture conditions, the distinction between the different hydrotope units is highest, whereas extremely dry or wet conditions tend to have a homogenizing effect on the spatial soil moisture distribution. This paper confirms that well-defined hydrotope units yield an improvement at pixel-scale soil moisture averages that can easily be applied. View full abstract»

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  • An Efficient Contextual Algorithm to Detect Subsurface Fires With NOAA/AVHRR Data

    Publication Year: 2008 , Page(s): 2005 - 2015
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (756 KB) |  | HTML iconHTML  

    This paper deals with the potential application of National Oceanic and Atmospheric Administration (NOAA)/Advanced Very High Resolution Radiometer (AVHRR) data to detect subsurface fire (subsurface hotspots) by proposing an efficient contextual algorithm. Most of the solutions proposed to date are mainly focused on the problem of surface fires, and very few research works have been performed to develop techniques for the subsurface fire problem. Although few algorithms based on the fixed-thresholding approach have been proposed for subsurface hotspot detection, however, for each application, thresholds have to be specifically tuned to cope with unique environmental conditions. The main objective of this paper is to develop an instrument-independent adaptive method by which direct threshold or multithreshold can be avoided. The proposed contextual algorithm is very helpful to monitor subsurface hotspots with operational satellite data, such as the Jharia region of India, without making any region-specific guess in thresholding. Novelty of the proposed work lies in the fact that once the algorithmic model is developed for the particular region of interest after optimizing the model parameters, there is no need to optimize those parameters again for further satellite images. Hence, the developed model can be used for optimized automated detection and monitoring of subsurface hotspots for future images of the particular region of interest. The algorithm is adaptive in nature and uses vegetation index and different NOAA/AVHRR channel's statistics to detect hotspots in the region of interest. The performance of the algorithm is assessed in terms of sensitivity and specificity and compared with other well-known thresholding techniques such as Otsu's thresholding, entropy-based thresholding, and existing contextual algorithm proposed by Flasse and Ceccato. The proposed algorithm is found to give better hotspot detection accuracy with lesser false alarm rate. View full abstract»

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  • Implementation and Evaluation of Concurrent Gradient Search Method for Reprojection of MODIS Level 1B Imagery

    Publication Year: 2008 , Page(s): 2016 - 2027
    Cited by:  Papers (19)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (781 KB) |  | HTML iconHTML  

    This paper presents details regarding implementation of a novel algorithm for reprojection of Moderate Resolution Imaging Spectroradiometer (MODIS) Level 1B imagery. The method is based on a simultaneous 2-D search in latitude and longitude geolocation fields by using their local gradients. Due to the segmented structure of MODIS imagery caused by the instrument whiskbroom electrooptical design, the gradient search is realized in the following two steps: intersegment and intrasegment search. This approach resolves the discontinuity of the latitude/longitude geolocation fields caused by overlap between consecutively scanned MODIS multidetector image segments. The structure of the algorithm allows equal efficiency with nearest neighbor and bilinear interpolation. A special procedure that combines analytical and numerical schemes is designed for reprojecting imagery near the polar region, where the standard gradient search may become unstable. The performance of the method was validated by comparison of reprojected MODIS/Terra and MODIS/Aqua images with georectified Landsat-7 Enhanced Thematic Mapper Plus imagery over Canada. It was found that the proposed method preserves the absolute geolocation accuracy of MODIS pixels determined by the MODIS geolocation team. The method was implemented to reproject MODIS Level 1B imagery over Canada, North America, and Arctic circumpolar zone in the following four popular geographic projections: Plate Care (cylindrical equidistant), Lambert Conic Conformal, Universal Transverse Mercator, and Lambert Azimuthal Equal-Area. It was also found to be efficient for reprojection of Advanced Very High Resolution Radiometer and Medium Resolution Imaging Spectrometer satellite images and general-type meteorological fields, such as the North American Regional Reanalysis data sets. View full abstract»

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  • Development of the Adjoint Model of a Canopy Radiative Transfer Model for Sensitivity Study and Inversion of Leaf Area Index

    Publication Year: 2008 , Page(s): 2028 - 2037
    Cited by:  Papers (14)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (666 KB) |  | HTML iconHTML  

    Many canopy reflectance models have been developed in the last decades and used for estimating land surface biogeophysical variables, such as leaf area index (LAI), from satellite observations through optimization procedures. In most studies, the derivative information of the canopy reflectance model has not been used effectively, which limits this approach for regional and global applications. The final solutions are often converged to the local minima. To address these issues, the adjoint model of a canopy radiative transfer model is developed in this study through the automatic differentiation technique. The developed adjoint model is used for sensitivity study, and a combination of the adjoint model with the trust region global optimization method is performed to retrieve LAI from the Enhanced Thematic Mapper Plus (ETM+). This study demonstrates that this method can be reliably used for inverting LAI efficiently and is suitable for global applications. View full abstract»

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  • Estimation of the Acoustic-to-Seismic Coupling Ratio Using a Moving Vehicle Source

    Publication Year: 2008 , Page(s): 2038 - 2043
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (492 KB) |  | HTML iconHTML  

    We present a simple passive technique for estimating the acoustic-to-seismic signal coupling ratio (SAR) in the ground using noise produced by moving vehicles. The seismic signal received on a geophone contains some energy that has propagated as seismic waves and some energy that couples from acoustic waves to seismic waves in the vicinity of the geophone. We use the frequency-domain coherence between the microphone and geophone signals to determine when the seismic signal is predominantly due to acoustic-to-seismic wave coupling. In frequency bands where the microphone and geophone coherence is above 0.8, the ratio of the seismic ground particle velocity to sound pressure-SAR-can be determined with less than 2 dB of error. The method is applied to data from a summer experiment with grass ground cover and at two winter experiments with snow-covered ground. At 100 Hz, the summer analysis yields a SAR value of 1.0 times 10-5 [(m/s)/Pa]. In addition, at 100 Hz, the two winter tests yield SAR between 0.1 times 10-5 and 1.0 times 10-5 [(m/s)/Pa]. In the later winter result, our vehicle-derived SAR estimate is shown to be in excellent agreement with SAR estimates obtained from blank pistol shots. Through the opportunistic exploitation background noise sources, our approach opens the possibility for automatic adaptation of unattended acoustic area, monitoring sensors to changing ground conditions. View full abstract»

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  • Least Squares-Based Filter for Remote SensingImage Noise Reduction

    Publication Year: 2008 , Page(s): 2044 - 2049
    Cited by:  Papers (8)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1041 KB) |  | HTML iconHTML  

    The Vondrak filter is a unique technique for smoothing data. The filter aims to achieve a balance between the fidelity and the smoothness of the filtered results. It can therefore preserve the original attributes of the observational data while, at the same time, smooth out the noise. We reformulate the 1-D Vondrak filter that has been widely used in data processing in fields such as astronomy and geophysics and then extend it into two dimensions. The method of conjugate gradients is used to solve the least squares optimization problem. The proposed 2-D filter is a powerful tool for enhancing the quality of various geoscience and remote sensing data such as satellite images. Various tests with simulated and real synthetic aperture radar interferograms show that the new filter is very effective in removing the noise. View full abstract»

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  • A Modified Approach for Noise Estimation in Optical Remotely Sensed Images With a Semivariogram: Principle, Simulation, and Application

    Publication Year: 2008 , Page(s): 2050 - 2060
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1346 KB) |  | HTML iconHTML  

    In this paper, a modified approach for noise estimation in optical remotely sensed images is developed under the framework of semivariogram (SV) technique in geostatistics, which is a fundamental and important task for on-ground quantitative application. In comparison with the original method, which involves the extrapolation of modeled SV to the ordinate, in the modified approach, the relationship between two different SVs of true objects at different lags is established. Simulation results show that the latter is more accurate and stable in estimating noise, particularly in the conditions of usual subimage sizes (i.e., 16 times 16 and 32 times 32) as well as in lower noise level. Moreover, the potential negative values in the original method no longer exist in the modified one. Additionally, after the removal of the analog- to-digital conversion noise effects, detection sensitivity evaluations and long-term surveillances for on-orbit optical remotely sensed instrument, for example FY-2 visible infrared spin-scan radiometer, are performed successfully, and the absolute error for noise- equivalent delta temperature estimation is within 0.05 K at 300 K. A common and feasible way for estimating nugget variance of SV in geostatistics is proposed in this paper with the assumption of stationary for both objects and noise process. View full abstract»

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  • Noise Removal From Hyperspectral Images by Multidimensional Filtering

    Publication Year: 2008 , Page(s): 2061 - 2069
    Cited by:  Papers (29)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1021 KB) |  | HTML iconHTML  

    A generalized multidimensional Wiener filter for denoising is adapted to hyperspectral images (HSIs). Commonly, multidimensional data filtering is based on data vectorization or matricization. Few new approaches have been proposed to deal with multidimensional data. Multidimensional Wiener filtering (MWF) is one of these techniques. It considers a multidimensional data set as a third-order tensor. It also relies on the separability between a signal subspace and a noise subspace. Using multilinear algebra, MWF needs to flatten the tensor. However, flattening is always orthogonally performed, which may not be adapted to data. In fact, as a Tucker-based filtering, MWF only considers the useful signal subspace. When the signal subspace and the noise subspace are very close, it is difficult to extract all the useful information. This may lead to artifacts and loss of spatial resolution in the restored HSI. Our proposed method estimates the relevant directions of tensor flattening that may not be parallel either to rows or columns. When rearranging data so that flattening can be performed in the estimated directions, the signal subspace dimension is reduced, and the signal-to-noise ratio is improved. We adapt the bidimensional straight-line detection algorithm that estimates the HSI main directions, which are used to flatten the HSI tensor. We also generalize the quadtree partitioning to tensors in order to adapt the filtering to the image discontinuities. Comparative studies with MWF, wavelet thresholding, and channel-by-channel Wiener filtering show that our algorithm provides better performance while restoring impaired HYDICE HSIs. View full abstract»

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  • A Novel Approach to Unsupervised Change Detection Based on a Semisupervised SVM and a Similarity Measure

    Publication Year: 2008 , Page(s): 2070 - 2082
    Cited by:  Papers (40)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (929 KB) |  | HTML iconHTML  

    This paper presents a novel approach to unsupervised change detection in multispectral remote-sensing images. The proposed approach aims at extracting the change information by jointly analyzing the spectral channels of multitemporal images in the original feature space without any training data. This is accomplished by using a selective Bayesian thresholding for deriving a pseudotraining set that is necessary for initializing an adequately defined binary semisupervised support vector machine classifier. Starting from these initial seeds, the performs change detection in the original multitemporal feature space by gradually considering unlabeled patterns in the definition of the decision boundary between changed and unchanged pixels according to a semisupervised learning algorithm. This algorithm models the full complexity of the change-detection problem, which is only partially represented from the seed pixels included in the pseudotraining set. The values of the classifier parameters are then defined according to a novel unsupervised model-selection technique based on a similarity measure between change-detection maps obtained with different settings. Experimental results obtained on different multispectral remote-sensing images confirm the effectiveness of the proposed approach. View full abstract»

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  • Preprocessing of Low-Resolution Time Series Contaminated by Clouds and Shadows

    Publication Year: 2008 , Page(s): 2083 - 2096
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1837 KB) |  | HTML iconHTML  

    Monitoring changes in the vegetation cover during the intercrop season is of special interest in intensive agricultural region, such as the Brittany region in France, to locate bare soils and control their influence to the environment. The presence of bare soils leads to detrimental environmental effects such as soil erosion or water quality degradation. Therefore, identification and monitoring of bare soils at a regional scale in the winter season are required for any agricultural management program. Data from the Moderate Resolution Imaging Spectroradiometer have been selected for this paper due to their low spatial resolution, which decreases the cost of processing and storage, and the high revisit frequency, which increases the probability to acquire scenes free of clouds and shadows during the winter season. Unfortunately, few images per season only are free of cloud contamination and associated shadows. Therefore, the specific objective of this paper is to develop and implement a preprocessing method to recover spectral values of contaminated data by weather conditions for subsequent bare-soil mapping. In the context of this paper, Kohonen's self-organizing map (SOM) is used for recovering data contaminated by weather conditions that have been considered as erroneous data. This nonparametric regression procedure has proved its success to deal with missing-values problem. Hence, the erroneous-values problem, reflectance values contaminated by clouds or shadows, has been converted to the missing-values problem by using a cloud and shadow (outlier) detector. The SOM algorithm was tested also on the erroneous data directly, but better results were found with the ldquomissing valuesrdquo formulation. The idea is to, first, train SOM onto clear temporal profiles free of clouds and shadows during the winter season. Second, erroneous values are converted to missing values by an outlier detector which operates on each temporal profile (set of colocated pixels acquired- - at different dates). Finally, the SOM algorithm for missing values is used to estimate contaminated reflectance values. View full abstract»

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  • Automatic Detection of Geospatial Objects Using Multiple Hierarchical Segmentations

    Publication Year: 2008 , Page(s): 2097 - 2111
    Cited by:  Papers (58)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3147 KB) |  | HTML iconHTML  

    The object-based analysis of remotely sensed imagery provides valuable spatial and structural information that is complementary to pixel-based spectral information in classification. In this paper, we present novel methods for automatic object detection in high-resolution images by combining spectral information with structural information exploited by using image segmentation. The proposed segmentation algorithm uses morphological operations applied to individual spectral bands using structuring elements in increasing sizes. These operations produce a set of connected components forming a hierarchy of segments for each band. A generic algorithm is designed to select meaningful segments that maximize a measure consisting of spectral homogeneity and neighborhood connectivity. Given the observation that different structures appear more clearly at different scales in different spectral bands, we describe a new algorithm for unsupervised grouping of candidate segments belonging to multiple hierarchical segmentations to find coherent sets of segments that correspond to actual objects. The segments are modeled by using their spectral and textural content, and the grouping problem is solved by using the probabilistic latent semantic analysis algorithm that builds object models by learning the object-conditional probability distributions. The automatic labeling of a segment is done by computing the similarity of its feature distribution to the distribution of the learned object models using the Kullback-Leibler divergence. The performances of the unsupervised segmentation and object detection algorithms are evaluated qualitatively and quantitatively using three different data sets with comparative experiments, and the results show that the proposed methods are able to automatically detect, group, and label segments belonging to the same object classes. 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