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Geoscience and Remote Sensing, IEEE Transactions on

Issue 2 • Date Feb. 2006

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  • [Front cover]

    Publication Year: 2006 , Page(s): c1
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  • IEEE Transactions on Geoscience and Remote Sensing publication information

    Publication Year: 2006 , Page(s): c2
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  • Table of contents

    Publication Year: 2006 , Page(s): 249 - 250
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  • Mutual induction and the effect of host conductivity on the EM induction response of buried plate targets using 3-D finite-element analysis

    Publication Year: 2006 , Page(s): 251 - 259
    Cited by:  Papers (8)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (536 KB)  

    A finite-element analysis of electromagnetic induction (EMI) in the presence of multiple buried metal targets is undertaken for the purpose of unexploded ordnance (UXO) detection and discrimination. The effects of mutual coupling between metal targets and the host conductivity are shown to be important. At high frequencies, mutual coupling is strong, and effects of host conductivity are relatively minor. At lower frequencies near the resistive limit, EMI responses are very small, but the effect of host conductivity becomes important. This is due to the galvanic current flow in the host medium that dissipates charge accumulations on the host/target interfaces. Qualitative analysis of induced current patterns in metal targets demonstrates that mutual coupling is strongly affected by target orientation and skin depth. Rigorous forward modeling of EMI responses is essential to understanding UXO sensor signatures so that discrimination between live UXO items and harmless fragments and clutter may become possible. View full abstract»

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  • An abrupt change detection algorithm for buried landmines localization

    Publication Year: 2006 , Page(s): 260 - 272
    Cited by:  Papers (13)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1152 KB) |  | HTML iconHTML  

    Ground-penetrating radars (GPRs) are very promising sensors for landmine detection as they are capable of detecting landmines with low metal contents. GPRs deliver so-called Bscan data which are, roughly, vertical slice images of the ground. However, due to the high dielectric permittivity contrast at the air-ground interface, a strong response is recorded at early time by GPRs. This response is the main component of the so-called clutter noise and it blurs the responses of landmines buried at shallow depths. The landmine detection task is therefore quite difficult. This paper proposes a new method for automated detection and localization of buried objects from Bscan records. A support vector machine algorithm for online abrupt change detection is implemented and proves to be efficient in detecting buried landmines from Bscan data. The proposed procedure performance is evaluated using simulated and real data. View full abstract»

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  • Monolithic folded pendulum accelerometers for seismic monitoring and active isolation systems

    Publication Year: 2006 , Page(s): 273 - 276
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (864 KB) |  | HTML iconHTML  

    A new class of very low noise low-frequency force-balance accelerometers is presented. The device has been designed for advanced mirror isolation systems of interferometric gravitational wave detectors. The accelerometer consists of a small monolithic folded pendulum with 2 s of natural period and an in-vacuum mechanical quality factor of 3000. The folded pendulum geometry, combined with the monolithic design, allows a unique 0.01% cross-axis residual coupling. Equipped with a high-resolution capacitance position sensor, it is capable of a noise-equivalent inertial displacement of 1-nm root mean square integrated over all the frequencies above 0.01 Hz. The main features of this new accelerometer are here reviewed. New possible applications of monolithic folded pendula in geophysical instrumentation are discussed. View full abstract»

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  • Accuracy assessment of SAR data-based snow-covered area estimation method

    Publication Year: 2006 , Page(s): 277 - 287
    Cited by:  Papers (11)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1096 KB)  

    Employment of satellite radar-based remote sensing data for snow monitoring during the snow melt season has been widely studied by several investigators. Several methods for the estimation of snow-covered area (SCA) fraction have been developed for different types of regions. One common deficiency with the SCA estimation methods has been the lack of statistical accuracy analyses for them. In order to incorporate SCA estimates for operational use, one vital requisite is a thorough statistical analysis of the SCA estimation accuracy. This shortcoming has been addressed for boreal forest region, as an extensive statistical accuracy analysis has been carried out for the Helsinki University of Technology (TKK)-developed SCA method. The TKK SCA method was developed for boreal forest regions, and it is studied here with 24 European Remote Sensing 2 synthetic aperture radar intensity images, on a boreal-forest-dominated test area located in northern Finland. The performance of the SCA method is investigated by using reference data acquired through hydrological modeling. The accuracy analysis is carried out for several statistical variables, and the statistical interpretation is done with respect to several affecting parameters. The accuracy analysis shows a high correlation coefficient between the SCA estimates and the reference data and root mean square error values of 0.213 for open areas and 0.179 for forested areas. In addition, the TKK method employs two reference images for the SCA estimation, and the usability of multiyear reference image utilization was analyzed and proven feasible in this study. View full abstract»

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  • Image autocoregistration and InSAR interferogram estimation using joint subspace projection

    Publication Year: 2006 , Page(s): 288 - 297
    Cited by:  Papers (29)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1824 KB)  

    In this paper, we propose a new method to estimate synthetic aperture radar interferometry (InSAR) interferometric phase in the presence of large coregistration errors. The method takes advantage of the coherence information of neighboring pixel pairs to automatically coregister the SAR images and employs the projection of the joint signal subspace onto the corresponding joint noise subspace to estimate the terrain interferometric phase. The method can automatically coregister the SAR images and reduce the interferometric phase noise simultaneously. Theoretical analysis and computer simulation results show that the method can provide accurate estimate of the terrain interferometric phase (interferogram) as the coregistration error reaches one pixel. The effectiveness of the method is also verified with the real data from the Spaceborne Imaging Radar-C/X Band SAR and the European Remote Sensing 1 and 2 satellites. View full abstract»

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  • An integrated multiscaling strategy based on a particle swarm algorithm for inverse scattering problems

    Publication Year: 2006 , Page(s): 298 - 312
    Cited by:  Papers (38)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1920 KB) |  | HTML iconHTML  

    The application of a multiscale strategy integrated with a stochastic technique to the solution of nonlinear inverse scattering problems is presented. The approach allows the explicit and effective handling of many difficulties associated with such problems ranging from ill-conditioning to nonlinearity and false solutions drawback. The choice of a finite dimensional representation for the unknowns, due to the upper bound to the essential dimension of the data, is iteratively accomplished by means of an adaptive multiresolution model, which offers a considerable flexibility for the use of the information on the scattering domain acquired during the iterative steps of the multiscaling process. Even though a suitable representation of the unknowns could limit the local minima problem, the multiresolution strategy is integrated with a customized stochastic optimizer based on the behavior of a particle swarm, which prevents the solution from being trapped into false solutions without a large increasing of the overall computational burden. Selected examples concerned with a two-dimensional microwave imaging problem are presented for illustrating the key features of the integrated stochastic multiscaling strategy. View full abstract»

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  • Microphysical characterization of microwave Radar reflectivity due to volcanic ash clouds

    Publication Year: 2006 , Page(s): 313 - 327
    Cited by:  Papers (16)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1288 KB)  

    Ground-based microwave radar systems can have a valuable role in volcanic ash cloud monitoring as evidenced by available radar imagery. Their use for ash cloud detection and quantitative retrieval has been so far not fully investigated. In order to do this, a forward electromagnetic model is set up and examined taking into account various operating frequencies such as S-, C-, X-, and Ka-bands. A dielectric and microphysical characterization of volcanic vesicular ash is carried out. Particle size-distribution (PSD) functions are derived both from the sequential fragmentation-transport (SFT) theory of pyroclastic deposits, leading to a scaled-Weibull PSD, and from more conventional scaled-Gamma PSD functions. Best fitting of these theoretical PSDs to available measured ash data at ground is performed in order to determine the value of the free PSD parameters. The radar backscattering from spherical-equivalent ash particles is simulated up to Ka-band and the accuracy of the Rayleigh scattering approximation is assessed by using an accurate ensemble particle scattering model. A classification scheme of ash average concentration and particle size is proposed and a sensitivity study of ash radar backscattering to model parameters is accomplished. A comparison with C-band radar signatures is finally illustrated and discussed. View full abstract»

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  • Extension of GPM dual-frequency iterative retrieval method with DSD-profile constraint

    Publication Year: 2006 , Page(s): 328 - 335
    Cited by:  Papers (6)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (552 KB) |  | HTML iconHTML  

    A new Dual-Frequency Precipitation Radar (DPR) will be included on the Global Precipitation Measurement (GPM) core satellite which will succeed the highly successful Tropical Rainfall Measuring Mission satellite launched in 1997. New dual-frequency drop-size distribution (DSD) and rain-rate estimation algorithms are being developed to take advantage of the enhanced capabilities of the DPR. It has been shown previously that a backward-iteration algorithm can be embedded within a single-loop feedback model to retrieve the rain rate. However, the single-loop model is unable to correctly estimate DSD profiles for a significant portion of global median-volume-diameter, Do, and normalized DSD intercept parameter, Nw, combinations in rain because of a multiple-value solution space. For the remaining Do,Nw pairs, another retrieval method is necessary. This paper proposes a dual-loop model, in which the intercept parameter, Nw, of the DSD is constrained in its vertical profile, to guide the algorithm to correct convergence. This allows an additional constraint on the DSD values estimated by the iterative algorithm and helps to retrieve correct DSD values in the regions where the iterative approach alone fails. To demonstrate feasibility of the proposed method, three test cases representative of many DSD and profile combinations are discussed. The first case is a constant vertical profile of the DSD parameters. The second case examines linear variation of the DSD parameters, and the third case examines how measurement error affects the retrieval process. In each case, the proposed constraint on the intercept parameter is implemented, and the results are discussed. Using the constraint, the dual-loop algorithm is able to retrieve reasonable values for the DSDs and rain-rate profiles and extend the convergence region of the algorithm. View full abstract»

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  • Variational approaches on discontinuity localization and field estimation in sea surface temperature and soil moisture

    Publication Year: 2006 , Page(s): 336 - 350
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2296 KB) |  | HTML iconHTML  

    Some applications in remote sensing require estimating a field containing a discontinuity whose exact location is a priori unknown. Such fields of interest include sea surface temperature in oceanography and soil moisture in hydrology. For the former, oceanic fronts form a temperature discontinuity, while in the latter sharp changes exist across the interface between soil types. To complicate the estimation process, remotely sensed measurements often exhibit regions of missing observations due to occlusions such as cloud cover. Similarly, water surface and ground-based sensors usually provide only an incomplete set of measurements. Traditional methods of interpolation and smoothing for estimating the fields from such potentially sparse measurements often blur across the discontinuities in the field. View full abstract»

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  • Cole-cole dispersion models for aqueous gelatin-syrup dielectric composites

    Publication Year: 2006 , Page(s): 351 - 355
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (344 KB) |  | HTML iconHTML  

    A dielectric composite is developed with permittivity ranging from 8 to 75 with selectable conductive and dielectric losses. The composite comprises gelatin, high fructose corn syrup (HFCS), NaCl, and water, and can be used to model soils, loams, and sands in the 200-MHz to 20-GHz range. A single-term Cole-Cole dispersion equation is developed with frequency-independent parameters being functions of component concentrations. Fits are provided for various soil samples and surrogate concentrations. View full abstract»

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  • PolInSAR analysis of X-band data over vegetated and urban areas

    Publication Year: 2006 , Page(s): 356 - 364
    Cited by:  Papers (16)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (800 KB) |  | HTML iconHTML  

    This paper investigates the polarimetric and polarimetric interferometric synthetic aperture radar (PolInSAR) information contained in the high-resolution X-band data acquired by the RAMSES airborne SAR system over an area around Avignon, France containing bare surfaces, vegetation, and urban areas. The interferometric coherences are computed over natural and urban areas for all possible baseline copolar polarizations. In the complex plane, the obtained regions of coherence corresponding to most vegetation areas display small angular extents, meaning that if penetration occurs in the foliage, it is shallower than the system height accuracy. To quantify the PolInSAR information, an analysis of the interferometric height accuracy is first performed, and the results are compared with those associated with a theoretical and an empirical model. Concerning vegetation, a 6-m height difference is measured between the different polarimetric phase centers over a sparse pine forest, probably due to the presence of holes in the canopy. Crop study reveals also that wheat-type fields present oriented media properties at X-band due to their vertical structure. Over urban areas, in most cases, building height can be accurately obtained by using Pauli polarimetric phase center information. View full abstract»

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  • A directional spectral mixture analysis method: application to multiangular airborne measurements

    Publication Year: 2006 , Page(s): 365 - 377
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2288 KB)  

    This study aims at developing an operational approach-namely, directional spectral mixture analysis (DISMA)-for retrieving vegetation parameters like fractional vegetation cover (FVC) and leaf area index (LAI) from multispectral and multiangular data. The approach attempts to highlight the consistency of one-dimensional models and linear mixture approaches. DISMA combines spectral signatures of soil and vegetation components with an analytical approximation of the radiative transfer equation, giving rise to a fast invertible bidirectional reflectance distribution function (BRDF) model of discontinuous canopies. Both the forward model and its inversion using a simple technique based on lookup tables method are tested using airborne POLDER and HyMap data corresponding to cropland. The method has proven fast enough to image vegetation properties over large areas, providing accurate and stable maps of FVC and LAI. The retrievals of LAI correspond well with ground-based measurements of specific crops, with root mean square error differences of 0.5-0.6 and a r2 of the linear fitting around 0.92. Though the accuracy assessment of retrieved parameters may be sensitive to the BRDF sampling, the results of model inversion remain consistent when varying the angular and spectral configurations. A model intercomparison exercise has been carried out using different models, either purely descriptive or based on radiative transfer modeling, indicating that this general approach is both sound and consistent. Thus, DISMA appears to be a useful tool for exploiting the synergistic spectral and angular potential of the new sensors. View full abstract»

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  • Weighted abundance-constrained linear spectral mixture analysis

    Publication Year: 2006 , Page(s): 378 - 388
    Cited by:  Papers (26)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1136 KB) |  | HTML iconHTML  

    Linear spectral mixture analysis (LSMA) has been used in a wide range of applications. It is generally implemented without constraints due to mathematical tractability. However, it has been shown that constrained LSMA can improve unconstrained LSMA, specifically in quantification when accurate estimates of abundance fractions are necessary. As constrained LSMA is considered, two constraints are generally imposed on abundance fractions, abundance sum-to-one constraint (ASC) and abundance nonnegativity constraint (ANC), referred to as abundance-constrained LSMA (AC-LSMA). A general and common approach to solving AC-LSMA is to estimate abundance fractions in the sense of least squares error (LSE) while satisfying the imposed constraints. Since the LSE resulting from each individual band in abundance estimation is not weighted in accordance with significance of bands, the effect caused by the LSE is then assumed to be uniform over all the bands, which is generally not necessarily true. This paper extends the commonly used AC-LSMA to three types of weighted AC-LSMA resulting from three different signal processing perspectives, parameter estimation, pattern classification, and orthogonal subspace projection. As demonstrated by experiments, the weighted AC-LSMA generally performs better than unweighted AC-LSMA which can be considered as a special case of our proposed weighted AC-LSMA with the weighting matrix chosen to be the identity matrix. View full abstract»

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  • On the effect of variable endmember spectra in the linear mixture model

    Publication Year: 2006 , Page(s): 389 - 396
    Cited by:  Papers (18)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (248 KB) |  | HTML iconHTML  

    The linear mixture model is frequently used to characterize surface cover over land, to model the reflectance of heterogeneous surfaces, and, by inversion, to estimate fractional cover from a multispectral satellite signal. It is usually assumed that certain parameters of this model, namely the so-called endmember spectra, are fixed, and that the model residual - the difference between a signal and its expected value in terms of the linear model - is systematically independent of all other parameters. In a small number of studies the endmember spectra have been allowed to have random fluctuations, giving rise to a covariance matrix for the residual that depends on the underlying proportions, and two distinct models exist for this mixed-pixel covariance matrix. In this note the linear model for mixed pixels is examined with varying endmember spectra, and it is shown that under a simple set of models for the variability of both endmembers and abundance, the covariance matrix for the residual is a weighted sum of the two previously considered cases. Generally, the balance between the two limiting cases is determined by the length scale for changes in the reflectance of any given cover type, and the length scale for changes in surface cover itself; one or other of the two limit models is preferred when these lengths are very different. View full abstract»

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  • Noise reduction of hyperspectral imagery using hybrid spatial-spectral derivative-domain wavelet shrinkage

    Publication Year: 2006 , Page(s): 397 - 408
    Cited by:  Papers (52)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2040 KB) |  | HTML iconHTML  

    In this paper, a new noise reduction algorithm is introduced and applied to the problem of denoising hyperspectral imagery. This algorithm resorts to the spectral derivative domain, where the noise level is elevated, and benefits from the dissimilarity of the signal regularity in the spatial and the spectral dimensions of hyperspectral images. The performance of the new algorithm is tested on two different hyperspectral datacubes: an Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) datacube that is acquired in a vegetation-dominated site and a simulated AVIRIS datacube that simulates a geological site. The new algorithm provides signal-to-noise-ratio improvement up to 84.44% and 98.35% in the first and the second datacubes, respectively. View full abstract»

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  • A novel approach for spectral unmixing, classification, and concentration estimation of chemical and biological agents

    Publication Year: 2006 , Page(s): 409 - 419
    Cited by:  Papers (24)  |  Patents (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (728 KB) |  | HTML iconHTML  

    In this paper, spectral unmixing methods, which are extensively used in hyperspectral imaging area, are proposed for classification and abundance fraction (concentration) estimation of chemical and biological agents that exist in the mixture form. Several government-furnished datasets, which were collected through the infrared spectrum method, were thoroughly analyzed. Two similarity measures-the spectral angle mapper and spectral information divergence-were investigated in order to provide a quantitative comparison basis with respect to the performance of the applied spectral unmixing methods in the existence of similar and distinct agents. The use of the similarity measures provided valuable information about the signature characteristics of the agents, which led to a better understanding about the capabilities of the investigated methods. The orthogonal subspace projection (OSP) method was investigated as the first unmixing, classification, and abundance estimation technique. It was observed that the OSP method provided good results when the number of agents in the database was small and was composed of distinct agents. However, when the number of agents was incremented by adding agents that share similar characteristics, the abundance estimation accuracy gradually degraded in addition to generating negative abundance fraction estimates. The second investigated unmixing method was called nonnegatively constrained least squares (NCLS). The results and analyses indicated that the NCLS method outperformed the OSP approach by providing considerably more accurate fraction estimates while at the same time not generating any negative fraction estimates; thus, the use of the NCLS method was found to be promising in detection and abundance fraction estimation of chemical and biological agents that exist in the form of mixtures. In addition, efficient implementation of NCLS has resulted in much lower computations than the conventional OSP implementation. View full abstract»

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  • An unsupervised artificial immune classifier for multi/hyperspectral remote sensing imagery

    Publication Year: 2006 , Page(s): 420 - 431
    Cited by:  Papers (46)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2512 KB) |  | HTML iconHTML  

    A new method in computational intelligence namely artificial immune systems (AIS), which draw inspiration from the vertebrate immune system, have strong capabilities of pattern recognition. Even though AIS have been successfully utilized in several fields, few applications have been reported in remote sensing. Modern commercial imaging satellites, owing to their large volume of high-resolution imagery, offer greater opportunities for automated image analysis. Hence, we propose a novel unsupervised machine-learning algorithm namely unsupervised artificial immune classifier (UAIC) to perform remote sensing image classification. In addition to their nonlinear classification properties, UAIC possesses biological properties such as clonal selection, immune network, and immune memory. The implementation of UAIC comprises two steps: initially, the first clustering centers are acquired by randomly choosing from the input remote sensing image. Then, the classification task is carried out. This assigns each pixel to the class that maximizes stimulation between the antigen and the antibody. Subsequently, based on the class, the antibody population is evolved and the memory cell pool is updated by immune algorithms until the stopping criterion is met. The classification results are evaluated by comparing with four known algorithms: K-means, ISODATA, fuzzy K-means, and self-organizing map. It is shown that UAIC is an adaptive clustering algorithm, which outperforms other algorithms in all the three experiments we carried out. View full abstract»

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  • Unsupervised change detection on SAR images using fuzzy hidden Markov chains

    Publication Year: 2006 , Page(s): 432 - 441
    Cited by:  Papers (40)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2376 KB) |  | HTML iconHTML  

    This work deals with unsupervised change detection in temporal sets of synthetic aperture radar (SAR) images. We focus on one of the most widely used change detector in the SAR context, the so-called log-ratio. In order to deal with the classification issue, we propose to use a new fuzzy version of hidden Markov chains (HMCs), and thus to address fuzzy change detection with a statistical approach. The main characteristic of the proposed model is to simultaneously use Dirac and Lebesgue measures at the class chain level. This allows the coexistence of hard pixels (obtained with the classical HMC segmentation) and fuzzy pixels (obtained with the fuzzy measure) in the same image. The quality assessment of the proposed method is achieved with several bidate sets of simulated images, and comparisons with classical HMC are also provided. Experimental results on real European Remote Sensing 2 Precision Image (ERS-2 PRI) images confirm the effectiveness of the proposed approach. View full abstract»

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  • Contextual reconstruction of cloud-contaminated multitemporal multispectral images

    Publication Year: 2006 , Page(s): 442 - 455
    Cited by:  Papers (31)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1216 KB) |  | HTML iconHTML  

    The frequent presence of clouds in passive remotely sensed imagery severely limits its regular exploitation in various application fields. Thus, the removal of cloud cover from this imagery represents an important preprocessing task consisting in the reconstruction of cloud-contaminated data. The intent of this study is to propose two novel general methods for the reconstruction of areas obscured by clouds in a sequence of multitemporal multispectral images. Given a cloud-contaminated image of the sequence, each area of missing measurements is reconstructed through an unsupervised contextual prediction process that reproduces the local spectro-temporal relationships between the considered image and an opportunely selected subset of the remaining temporal images. In the first method, the contextual prediction process is implemented by means of an ensemble of linear predictors, each trained over a local multitemporal region that is spectrally homogeneous in each temporal image of the selected subset. In order to obtain such regions, each temporal image is locally classified by an unsupervised classifier based on the expectation-maximization (EM) algorithm. In the second method, the local spectro-temporal relationships are reproduced by a single nonlinear predictor based on the support vector machines (SVM) approach. To illustrate the performance of the two proposed methods, an experimental analysis on a sequence of three temporal images acquired by the Landsat-7 Enhanced Thematic Mapper Plus sensor over a total period of four months is reported and discussed. It includes a detailed simulation study that aims at assessing with different reconstruction quality criteria the accuracy of the methods in different qualitative and quantitative cloud contamination conditions. Compared with two techniques based on compositing algorithms for cloud removal, the proposed methods show a clear superiority, which makes them a promising and useful tool in solving the considered problem, whose great complexity is commensurate with its practical importance. View full abstract»

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  • Spatiotriangulation with multisensor HR stereo-images

    Publication Year: 2006 , Page(s): 456 - 462
    Cited by:  Papers (8)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2712 KB) |  | HTML iconHTML  

    The objective of this research study was to evaluate the spatiotriangulation applied to multisensor high-resolution satellite stereo-images, which enabled the simultaneous geometric processing of a large number of stereo-pairs together to reduce the control point requirement. The spatiotriangulation is based on the three-dimensional physical models developed for multisensor images at the Canada Centre for Remote Sensing, Natural Resources Canada and on a least squares block stereo-bundle adjustment process with orbital constraints. The spatiotriangulation was applied to five along-/across-track stereo-images [panchromatic Syste`me Pour l'Observation de la Terre 5 (SPOT-5) High-Resolution-Stereoscopy (HRS) and High-Resolution-Geometry (HRG), Ikonos, and QuickBird] acquired over Quebec, Canada. The first results of stereo/block bundle adjustment showed that the same error residuals than the input data errors (1/2 to 1-2 pixels) were obtained depending of the stereo-images, whether independently or simultaneously processed. The second and most important results were related to simultaneous stereo-bundle adjustments of the largest "master" SPOT-5 stereo-pair (either HRS or HRG) using 12 ground control points (GCPs) and the smallest "slave" stereo-pair(s) using no GCP but only stereo tie-points (TPs). Better results were normally obtained with SPOT-5-HRG (5-m resolution) as the "master" stereo-pair due to less difference in the sensors resolution than with SPOT-5 HRS. The root mean square errors, verified by independent check points (ICPs) belonging only to the "slave" stereo-pairs, were around 2 m in the three axes. However, the combined image pointing and map errors of ICPs (1-2 m) are included in these 2-m error results, and the internal accuracy of the stereo-pairs should thus be better (less than one resolution). The research study demonstrated thus the possibility to use the largest stereo-pair with a reduced number of GCPs to simultaneously adjust single or multiple stereo-pair(s) with only stereo TPs, and with no degradation in the accuracy. View full abstract»

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  • Special issue on image information mining in earth observations-concepts, methods, systems, and applications

    Publication Year: 2006 , Page(s): 463
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    Freely Available from IEEE
  • Special issue on subsurface sensing using ground penetrating radar (GPR)

    Publication Year: 2006 , Page(s): 464
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    Freely Available from IEEE

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.

 

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Meet Our Editors

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