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Geoscience and Remote Sensing, IEEE Transactions on

Issue 7 • Date July 2005

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Displaying Results 1 - 25 of 32
  • [Front cover]

    Page(s): c1
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    Freely Available from IEEE
  • IEEE Transactions on Geoscience and Remote Sensing publication information

    Page(s): c2
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  • Table of contents

    Page(s): 1441 - 1442
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  • A noise model for estimated synthetic aperture radar look cross spectra acquired over the ocean

    Page(s): 1443 - 1452
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (592 KB) |  | HTML iconHTML  

    It is well known that look cross spectra processed from synthetic aperture radar (SAR) contain valuable information on ocean waves. With the launch of the European satellite ENVISAT, SAR look cross spectra (SLCS) have become available on an operational basis. Activities therefore exist at different European weather centres to use the data for assimilation into numerical wave models. Furthermore there is scientific interest in SLCS, e.g., concerning the estimation of the phase speed of ocean waves. For the estimation of ocean wave parameters, it is important to have information about the accuracy of SLCS. In this paper, errors of estimated SLCS due to SAR image speckle, spectral estimation errors, and image pattern decorrelation associated with ocean wave motion are analyzed. A probability model is proposed for the estimated SLCS based on the respective cross-spectrum coherence. The model is used to calculate signal-to-noise ratios and confidence limits for the SLCS phase and magnitude, as well as the real and imaginary part. The coherence is factored into a component describing look decorrelation due to SAR image speckle and a second factor describing the effect of sea surface motion. It is shown that the ocean-wave-dependent decorrelation can be simulated using existing nonlinear integral transforms for the look variance spectrum and the SLCS. The decorrelation effect associated with speckle noise is related to SAR system parameters, e.g., the spatial SAR resolution. The probability model is used to investigate the optimal choice of look processing parameters like the look separation time. A statistical analysis based on a global dataset of a reprocessed dataset of European Remote Sensing 2 satellite SLCS is presented confirming the applicability of the probability model. The implications of the results for the retrieval of two-dimensional wave spectra from SLCS are summarized. Possible future applications of the model like, for example, the investigation of the turbulent air flow over waves, are discussed. View full abstract»

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  • A new wind vector algorithm for C-band SAR

    Page(s): 1453 - 1458
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (504 KB) |  | HTML iconHTML  

    Ocean wind speed and wind direction are estimated simultaneously using the normalized radar cross sections σ0 corresponding to two neighboring (25-km) blocks, within a given synthetic aperture radar (SAR) image, having slightly different incidence angles. This method is motivated by the methodology used for scatterometer data. The wind direction ambiguity is removed by using the direction closest to that given by a buoy or some other source of information. We demonstrate this method with 11 ENVISAT Advanced SAR sensor images of the Gulf of Mexico and coastal waters of the North Atlantic. Estimated wind vectors are compared with wind measurements from buoys and scatterometer data. We show that this method can surpass other methods in some cases, even those with insufficient visible wind-induced streaks in the SAR images, to extract wind vectors. View full abstract»

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  • Permanent scatterers analysis for atmospheric correction in ground-based SAR interferometry

    Page(s): 1459 - 1471
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2328 KB) |  | HTML iconHTML  

    Ground-based synthetic aperture radar (GB-SAR) interferometry has already been recognized as a powerful tool, complementary or alternative to spaceborne SAR interferometry, for terrain monitoring, and for detecting structural changes in buildings. It has been noted that, in spite of the very short range, compared with the satellite configuration, in GB-SAR measurement the disturbances due to atmospheric effects cannot be neglected either. The analysis of the interferometric phases of very coherent points, called permanent scatterers (PSs), allows the evaluation of the atmospheric disturbance and the possibility of removing it. In this paper, the PS analysis is carried out both on a test site facility and on a real campaign (Citrin Valley, Italy) that provided data with a temporal baseline of about ten months. View full abstract»

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  • An application of L-band synthetic aperture radar to tide height measurement

    Page(s): 1472 - 1478
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2504 KB) |  | HTML iconHTML  

    A method for measuring the tide height near the coast from L-band synthetic aperture radar (SAR) data is presented. Twenty-one coherent interferograms have been successfully constructed from Japanese Earth Resources Satellite 1 (JERS-1) SAR data obtained over oyster sea-farming structures. A coherence analysis of the 21 interferometric pairs showed that a perpendicular baseline of less than 3 km, with a temporal baseline within 500 days, are required to obtain a coherent pair, with a coherence higher than 0.25, in the study area. The coherent phases preserved in the interferograms showed a close relation with the sea level. The problem of phase unwrapping to restore an absolute tide height was overcome by introducing normalized image intensities. The radar measurements estimated by the proposed method were verified using tide gauge data, and comparison of the two datasets yielded a correlation coefficient R2 of 0.91, with a root mean square error of 5.76 cm. The results demonstrate that radar interferometry can be applied for a tide height measurement near the coast given sufficient structures that return off-nadir radar pulses to the antenna. The multipolarized L-band SAR system will provide better results, using only double-bounced signals, in the future. View full abstract»

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  • Computation of longwave electromagnetic response of nonhomogeneous media

    Page(s): 1479 - 1489
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (784 KB) |  | HTML iconHTML  

    A method for the numeric estimation of the effective permittivity of any nonhomogeneous medium that admits the effective medium approximation under the longwave approximation is presented. Media are modeled as inclusions embedded in a continuous matrix. We show how the potentials at the inclusion boundaries are sufficient information for the estimation of the effective permittivity. We also show efficient implementation techniques to estimate them computationally, either by Monte Carlo random walk or by relaxation. We provide numerical results for several regular two- and three-dimensional structures and show the dependence of the effective response on the shape of the inclusions and their spatial arrangement, and the influence of the percolation threshold. View full abstract»

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  • Optimizing receiver configurations for resolution of equivalent dipole polarizabilities in situ

    Page(s): 1490 - 1498
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (824 KB) |  | HTML iconHTML  

    Equivalent dipole polarizabilities are a succinct way to summarize the inductive response of an isolated conductive body at distances greater than the scale of the body. At any time lag or frequency, an equivalent dipole polarizability response is comprised of nine parameters: six specifying an equivalent dipole polarizability matrix (which is symmetric) and three specifying the apparent location of the body center. Smith and Morrison have given equations for calculating uncertainties in equivalent dipole polarizability and position based on analysis of an iterative linearized inversion. Here, the root mean squared uncertainty in polarizability is weighted and summed over a number of control points and minimized using an evolutionary algorithm for a number of instrument designs. Three families of designs are presented: single-transmitter systems for use on a two-dimensional grid of positions with negligible error in relative instrument location, two-transmitter systems for use on a line of positions with negligible error in relative instrument location, and three-transmitter systems for stand alone use. Results for the one- and two-transmitter systems are strongly degraded by errors in instrument position, whereas the three-transmitter systems are insensitive to instrument positioning errors. View full abstract»

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  • Planetary exploration using a small electromagnetic sensor

    Page(s): 1499 - 1506
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    A prototype broadband electromagnetic (EM) sensor, GEM-5, has been built and tested as a possible new probe for the future Mars rover to seek an ice-bonded layer at a given depth below the Martian surface. The sensor, with a vertical coaxial coil configuration, will measure the terrain resistivity and susceptibility to determine lateral variations in resistivity and magnetic susceptibility. The lateral variations will indicate regions of resistivity/susceptibility anomalies that may contain ice or water at depth. The forward solution for the sensor geometry over a layered formation and inverse algorithms to convert the EM data into the apparent susceptibility and resistivity are developed to investigate the ability of the sensor in detecting and resolving a buried (wet) ice layer in Mars-like geologic formations. Based on the simulated study, we find that the prototype sensor design should be able to resolve the lateral variations in resistivity/susceptibility under conditions of the Martian subsurface. View full abstract»

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  • Kalman filtering for enhanced landmine detection using quadrupole resonance

    Page(s): 1507 - 1516
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (480 KB) |  | HTML iconHTML  

    Quadrupole resonance (QR) is a novel technology recently applied to landmine detection. The detection process is specific to the chemistry of the explosive, and therefore is less susceptible to the types of false alarms experienced by metal detectors and ground-penetrating radars. Although QR is vulnerable to radio-frequency interference (RFI) when the sensor is deployed in the field, adaptive RFI mitigation can remove most of the RFI. In this paper, advanced signal processing algorithms applied to the postmitigation signal are studied to enhance explosive detection. A new Kalman filtering strategy is proposed to estimate and detect the QR signal in the postmitigation signal. The results using both simulated data and experimental data show that the proposed algorithm can provide robust landmine detection performance. View full abstract»

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  • Parameter sensitivity of soil moisture retrievals from airborne L-band radiometer measurements in SMEX02

    Page(s): 1517 - 1528
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1264 KB) |  | HTML iconHTML  

    Over the past two decades, successful estimation of soil moisture has been accomplished using L-band microwave radiometer data. However, remaining uncertainties related to surface roughness and the absorption, scattering, and emission by vegetation must be resolved before soil moisture retrieval algorithms can be applied with known and acceptable accuracy using satellite observations. Surface characteristics are highly variable in space and time, and there has been little effort made to determine the parameter estimation accuracies required to meet a given soil moisture retrieval accuracy specification. This study quantifies the sensitivities of soil moisture retrieved using an L-band single-polarization algorithm to three land surface parameters for corn and soybean sites in Iowa, United States. Model sensitivity to the input parameters was found to be much greater when soil moisture is high. For even moderately wet soils, extremely high sensitivity of retrieved soil moisture to some model parameters for corn and soybeans caused the retrievals to be unstable. Parameter accuracies required for consistent estimation of soil moisture in mixed agricultural areas within retrieval algorithm specifications are estimated. Given the spatial and temporal variability of vegetation and soil conditions for agricultural regions it seems unlikely that, for the single-frequency, single-polarization retrieval algorithm used in this analysis, the parameter accuracy requirements can be met with current satellite-based land surface products. We conclude that for regions with substantial vegetation, particularly where the vegetation is changing rapidly, any soil moisture retrieval algorithm that is based on the physics and parameterizations used in this study will require multiple frequencies, polarizations, or look angles to produce stable, reliable soil moisture estimates. View full abstract»

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  • Reflective properties of natural snow: approximate asymptotic theory versus in situ measurements

    Page(s): 1529 - 1535
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    Results of measurements of the bidirectional reflection function of snow for the solar zenith angle close to 54° are compared with a recently developed snow optical model based on the representation of snow grains as fractal particles. The model has a high accuracy out of the principal plane for the observation zenith angles smaller than 60°. However, the accuracy is reduced in the principal plane. Specular light reflection by partially oriented snow plates on the snow surface not accounted for by the model can play a role for measurements in the principal plane. The model discussed can be used for the grain size retrieval using both ground and spaceborne measurements of the snow reflectance. This is supported by a high accuracy of the model in a broad spectral range 545-2120 nm as demonstrated in this work. View full abstract»

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  • On the molecular-aerosol scattering coupling in remote sensing of aerosol from space

    Page(s): 1536 - 1541
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (384 KB) |  | HTML iconHTML  

    This work is devoted to studies of the influence of the molecular-aerosol coupling on the scalar approximation-based aerosol satellite remote sensing procedures in the ultraviolet-near-infrared spectral range. It was found that the coupling error must be accounted in the aerosol remote sensing problems based on the analysis of the backscattered ultraviolet light. View full abstract»

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  • Spatial scales of tropical precipitation inferred from TRMM microwave imager data

    Page(s): 1542 - 1551
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    The local spatial scales of tropical precipitating systems were studied using Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) rain rate imagery from the TRMM satellite. Rain rates were determined from TMI data using the Goddard Profiling (GPROF) Version 5 algorithm. Following the analysis of Ricciardulli and Sardeshmukh (RS), who studied local spatial scales of tropical deep convection using global cloud imagery (GCI) data, active precipitating months were defined alternatively as those having greater than either 0.1 mm/h or 1 mm/h of rain for more than 5% of the time. Spatial autocorrelation values of rain rate were subsequently computed on a 55×55 km grid for convectively active months from 1998 to 2002. The results were fitted to an exponential correlation model using a nonlinear least squares routine to estimate a spatial correlation length at each grid cell. The mean spatial scale over land was 90.5 km and over oceans was 122.3 km for a threshold of 0.1 mm/h of rain with slightly higher values for a threshold of 1 mm/h of rain. An error analysis was performed which showed that the error in these determinations was of order 2% to 10%. The results of this study should be useful in the design of convective schemes for general circulation models and for precipitation error covariance models for use in numerical weather prediction and associated data assimilation schemes. The results of the TMI study also largely concur with those of RS, although the more direct relationship between the TMI data and rain rate relative to the GCI imagery provide more accurate correlation length estimates. The results also confirm the strong impact of land in producing short spatial scale convective rain. View full abstract»

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  • Direct and inverse radiative transfer solutions for visible and near-infrared hyperspectral imagery

    Page(s): 1552 - 1562
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1256 KB) |  | HTML iconHTML  

    Two reciprocal direct and inverse radiative transfer models dealing with hyperspectral remote sensing in the visible-to-shortwave-infrared spectral domain are described in this paper. The first one, called COMANCHE, considers a flat and heterogeneous ground scene, with bidirectional reflectance effects, and computes spectral radiance hypercubes at the sensor level. Trapping and environment phenomena are take into account through specific optimized Monte Carlo modules. The reciprocal inverse algorithm, called COCHISE, considers a sensor-level hyperspectral image and retrieves the ground spectral reflectance distribution as well as the water vapor content. COCHISE removes the atmospheric and environment effects with the same modeling as COMANCHE, but consider however the Lambertian assumption for the ground reflectance. Both of the models are validated with existing radiative transfer codes (MODTRAN and AMARTIS, for instance), and also using experimental datasets from the Airborne Visible/Infrared Imaging Spectrometer. The comparisons show very good agreement regarding to the usual uncertainties involved insuche experiment. COCHISE is also applied on a additional dataset acquired by HyMap. View full abstract»

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  • An airborne radiometer for stratospheric water vapor measurements at 183 GHz

    Page(s): 1563 - 1570
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (864 KB) |  | HTML iconHTML  

    The Airborne Millimeter- and Submillimeter Observing System (AMSOS) is a total-power radiometer for observations of the 183.3-GHz water vapor rotational line, operated onboard a Learjet aircraft of the Swiss Air Force. The radiometer is also used to observe the 175.45-GHz ozone line in the other sideband. The neatly designed quasi optics provide a regular and narrow output beam with a half-power beam-width angle of 1.2° and efficient sideband switching. A λ/4-quasi-optical isolator is used for baseline reduction securing attenuation of internal reflections by more than 30 dB. A low noise temperature of the ambient-temperature-operating system (1900 K) and excellent target pointing (better than 0.1°) provide a good duty cycle and reliable calibration. A reliable control over the radiometer's operational parameters, like system stability and system temperatures, and higher automatization were required to come up with high demands of an onboard operation. The measured spectra look typical for the region and time where they were observed. View full abstract»

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  • Vicarious calibration of ADEOS-2 GLI visible to shortwave infrared bands using global datasets

    Page(s): 1571 - 1584
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1920 KB) |  | HTML iconHTML  

    We have developed a global vicarious calibration scheme for spaceborne ocean-color sensors, simulating top-of-atmosphere radiance globally using a radiative transfer model, SeaWiFS Level 3 eight-day mean products, and an in-water optical model. This is a relative calibration against two channels used to detect aerosol properties; however, it enables us to determine the spatial and temporal characteristics of the vicarious calibration coefficients (Kvc) without in situ observations. We applied this scheme to the NASDA Global Imager (GLI), which operated from January 25, 2003 to October 24, 2003. Kvc exhibited the following properties: (1) channel characteristics of 1.0-1.1 (GLI was lower than the simulation) in channels 1-9 (380-565 nm), nearly 1.0 in channels 10-19 (625-865 nm), and 0.91-0.98 in channels 24-29 (1050-2210 nm); (2) scan-angle dependency and its temporal changes in channels 1-3; and (3) scan-mirror side differences and temporal changes. Applying Kvc to GLI ocean-color processing produced outputs consistent with the ground observation data. This scheme is also useful for generating consistent products from different ocean-color sensors in orbit. View full abstract»

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  • Improved determination of coastal water constituent concentrations from MERIS data

    Page(s): 1585 - 1591
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1376 KB) |  | HTML iconHTML  

    The algorithm to derive the concentrations of coastal (case 2) water constituents from the Medium Resolution Imaging Spectrometer (European Space Agency satellite ENVISAT) is based on neural network (NN) technology. The NN not only transforms water leaving radiance reflectances with high efficiency into concentrations but also checks if its input is in the domain of reflectance spectra which were simulated for the training of the NN. Two NNs are trained with simulated reflectances: (1) invNN to emulate the inverse model (reflectances, geometry) → concentrations and (2) forwNN to emulate the forward model (concentrations, geometry) → reflectances. The invNN is used to obtain an estimate of the concentrations. These concentrations are fed into the forwNN, and the derived reflectances are compared with the measured reflectances. Deviations above a threshold are flagged. The paper describes a further improvement: the result obtained by invNN is used as a first guess to start a minimization procedure, which uses the forwNN iteratively to minimize the difference between the calculated reflectances and the measured ones. The procedure is very fast as it takes advantage of the Jacobian which is a byproduct of the NN calculation. View full abstract»

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  • High-resolution ocean color remote sensing of benthic habitats: a case study at the Roatan island, Honduras

    Page(s): 1592 - 1604
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    Natural resource managers clamor for detailed reef habitat maps for monitoring smaller scale disturbances in reef communities. Coastal ocean color remote sensing techniques permit benthic habitats to be explored with higher resolution than ever before. The objective of this research was to develop an accurate benthic habitat map for an area off the northwest coast of Roatan Island, Honduras, using high-resolution multispectral IKONOS data. Atmospheric (Rayleigh and aerosol path radiance) and water column corrections (water depth and water column attenuation) were applied to the imagery, making it a robust method for mapping benthic habitats. Water depth for each pixel was calculated based on a site-specific polynomial model. A mechanistic radiative transfer approach was developed that removed the confound effect of the water column (absorption and scattering) from the imagery to retrieve an estimate of the bottom reflectance (albedo). Albedos were ≤ 12% for seagrass benthos, 12% to 24% for coral areas, and ≥ 24% for sand-dominated areas. The retrieved bottom albedos were then used to classify the benthos, generating a detailed map of benthic habitats, followed by accuracy assessment. View full abstract»

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  • Automatic tool for the precise detection of upwelling and filaments in remote sensing imagery

    Page(s): 1605 - 1616
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3016 KB) |  | HTML iconHTML  

    The upward movement of cool and nutrient-rich waters toward the surface leads to horizontal alterations in the distribution of the physical, chemical, and biological properties. Remote sensing is being extensively applied to detect such coastal upwellings; however, the enormous amount of data daily generated obliges to develop automatic detection and prediction tools. The problem of identifying oceanographic mesoscale structures has been studied using a variety of image processing techniques; however, the outstanding difficulties encountered in the traditional approaches are the presence of noise, the fact that gradients are weak, the strong morphological variation, and the absence of a valid analytical model for the structures. In this context, the proposed automatic upwelling extraction methodology overcomes the preceding detection inconveniences and achieves a highly accurate structure extraction. This automatic technique is based on a coarse-segmentation methodology followed by a fine-detail growing process. The complete system has been validated over a database of 378 multisensorial images of years 2000 to 2003, and it has been applied to the detection and feature extraction of coastal upwellings and filaments in three areas with different characteristics, such as the Canary Islands, Cape Ghir, and the Alboran Sea, using imagery from the Advanced Very High Resolution Radiometer 2 and 3 sensors, the Sea-viewing Wide Field-of-view Sensor, and the Moderate Resolution Imaging Spectroradiometer sensor, demonstrating its effectiveness and robustness in a wide variety of climate conditions. View full abstract»

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  • A unified framework for MAP estimation in remote sensing image segmentation

    Page(s): 1617 - 1634
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1416 KB) |  | HTML iconHTML  

    A complete framework is proposed for applying the maximum a posteriori (MAP) estimation principle in remote sensing image segmentation. The MAP principle provides an estimate for the segmented image by maximizing the posterior probabilities of the classes defined in the image. The posterior probability can be represented as the product of the class conditional probability (CCP) and the class prior probability (CPP). In this paper, novel supervised algorithms for the CCP and the CPP estimations are proposed which are appropriate for remote sensing images where the estimation process might to be done in high-dimensional spaces. For the CCP, a supervised algorithm which uses the support vector machines (SVM) density estimation approach is proposed. This algorithm uses a novel learning procedure, derived from the main field theory, which avoids the (hard) quadratic optimization problem arising from the traditional formulation of the SVM density estimation. For the CPP estimation, Markov random field (MRF) is a common choice which incorporates contextual and geometrical information in the estimation process. Instead of using predefined values for the parameters of the MRF, an analytical algorithm is proposed which automatically identifies the values of the MRF parameters. The proposed framework is built in an iterative setup which refines the estimated image to get the optimum solution. Experiments using both synthetic and real remote sensing data (multispectral and hyperspectral) show the powerful performance of the proposed framework. The results show that the proposed density estimation algorithm outperforms other algorithms for remote sensing data over a wide range of spectral dimensions. The MRF modeling raises the segmentation accuracy by up to 10% in remote sensing images. View full abstract»

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  • Modeling trajectory of dynamic clusters in image time-series for spatio-temporal reasoning

    Page(s): 1635 - 1647
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (752 KB) |  | HTML iconHTML  

    During the last decades, satellites have acquired incessantly high-resolution images of many Earth observation sites. New products have arisen from this intensive acquisition process: high-resolution satellite image time-series (SITS). They represent a large data volume with a rich information content and may open a broad range of new applications. This paper presents an information mining concept which enables a user to learn and retrieve spatio-temporal structures in SITS. The concept is based on a hierarchical Bayesian modeling of SITS information content which enables us to link the interest of a user to specific spatio-temporal structures. The hierarchy is composed of two inference steps: an unsupervised modeling of dynamic clusters resulting in a graph of trajectories, and an interactive learning procedure based on graphs which leads to the semantic labeling of spatio-temporal structures. Experiments performed on a SPOT image time-series demonstrate the concept capabilities. View full abstract»

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  • Multisensor approach to automated classification of sea ice image data

    Page(s): 1648 - 1664
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    A multisensor data fusion algorithm based on a multilayer neural network is presented for sea ice classification in the winter period. The algorithm uses European Remote Sensing (ERS), RADARSAT synthetic aperture radar (SAR), and low-resolution television camera images and image texture features. Based on a set of in situ observations made at the Kara Sea, a neural network is trained, and its structure is optimized using a pruning method. The performance of the algorithm with different combinations of input features (sensors) is assessed and compared with the performance of a linear discriminant analysis (LDA)-based algorithm. We show that for both algorithms a substantial improvement can be gained by fusion of the three different types of data (91.2% for the neural network) as compared with single-source ERS (66.0%) and RADARSAT (70.7%) SAR image classification. Incorporation of texture increases classification accuracy. This positive effect of texture becomes weaker with increasing number of sensors (from 8.4 to 6.4 percent points for the use of two and three sensors, respectively). In view of the short training time and smaller number of adjustable parameters, this result suggests that semiparametric classification methods can be considered as a good alternative to the neural networks and traditional parametric statistical classifiers applied for the sea ice classification. View full abstract»

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  • Integration of optical and radar classifications for mapping pasture type in Western Australia

    Page(s): 1665 - 1681
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4960 KB) |  | HTML iconHTML  

    In this study, independent classifications of Landsat Thematic Mapper imagery and Jet Propulsion Laboratory AirSAR were combined to create an integrated classification of pasture and other vegetation types for a study area in the agricultural zone of Western Australia. The resulting classification combines greenness and brightness information from optical data with structure and water content information from synthetic aperture radar (SAR). Field observations of vegetation type, botanical composition, ground cover percentage, wet and dry biomass, canopy height, and soil water content were collected at 34 sites representing a range of pastures, browse shrubs, and crops. An unsupervised version of the Complex Wishart classification procedure, based on preserving scattering characteristics from the Freeman and Durden backscatter decomposition, was applied to the C-, L-, and P-band polarimetric SAR data. The optical classification was carried out using a principle component analysis on the green, red, and near-infrared bands and clustering on the basis of a class centroid distance measure and knowledge of ground targets. These two classification results were then fused together. Assessment of a confusion matrix using the individual sites showed that identification of more uniform, dense, and structurally distinct canopies was better than that of more diverse, sparse, and structurally ambiguous canopies, as the former were better represented by the canopy height attribute used in the SAR classification component. The optical classification enabled correction of SAR misclassification of vegetation due to surface roughness and soil moisture effects, or similar backscatter responses from herbaceous or arboreal canopies. The results show that simplification of vegetation into groups based upon properties with sensitive responses in both the optical and SAR domains, and combination of separate SAR and optical classifications, has potential for improving classification of diverse and heterogeneous herbaceous and browse cover in grazing lands. However, collection of ground calibration data must be at an appropriate spatial scale and include canopy and surface measurements directly related to backscatter mechanisms and spectral sensitivity. 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.

 

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Meet Our Editors

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