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Geoscience and Remote Sensing, IEEE Transactions on

Issue 12 • Date Dec. 2008

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Displaying Results 1 - 25 of 27
  • [Front cover]

    Publication Year: 2008 , Page(s): C1
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    Freely Available from IEEE
  • IEEE Transactions on Geoscience and Remote Sensing publication information

    Publication Year: 2008 , Page(s): C2
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    Freely Available from IEEE
  • Table of contents

    Publication Year: 2008 , Page(s): 3957 - 3958
    Save to Project icon | Request Permissions | PDF file iconPDF (51 KB)  
    Freely Available from IEEE
  • Editorial

    Publication Year: 2008 , Page(s): 3959
    Save to Project icon | Request Permissions | PDF file iconPDF (60 KB) |  | HTML iconHTML  
    Freely Available from IEEE
  • Improved Space-Based Moving Target Indication via Alternate Transmission and Receiver Switching

    Publication Year: 2008 , Page(s): 3960 - 3974
    Cited by:  Papers (35)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1320 KB) |  | HTML iconHTML  

    Ground moving target indication (GMTI) by space-based radar systems requires special antenna and data acquisition concepts to overcome the problem of discriminating target signals from clutter returns. Owing to the high satellite speed, the clutter contains a broad mixture of radial velocities within the antenna beam, leading to a large Doppler spread. Effective clutter suppression can solely be achieved by multiple aperture or phase center antennas. For space-based systems, however, the number of receiver channels connected to subapertures is very limited due to their weight, volume, and high data rates (current systems such as TerraSAR-X and RADARSAT-2 possess only two). This classical along-track interferometry architecture, in which the antenna is split into two halves, achieves only suboptimum GMTI performance. This paper presents and statistically analyzes an innovative approach to create additional independent phase centers to improve the GMTI performance considerably. The extra degrees of freedom are created by cyclical phase and amplitude switchings of the transmit/receive modules for transmitter and receiver between pulses, hence trading Doppler bandwidth for extra spatial diversity. In the first part of this paper, different strategies of spatial-temporal diversity are introduced and analyzed for realistic system parameters with respect to ambiguities and detection performance. The second part is concerned with the elaborate theoretical analysis of the relocation improvement for the spatial diversity approach. View full abstract»

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  • An Imaging HF GPR Using Stationary Antennas: Experimental Validation Over the Antarctic Ice Sheet

    Publication Year: 2008 , Page(s): 3975 - 3986
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1136 KB) |  | HTML iconHTML  

    Terrestrial And Planetary Imaging Radar (TAPIR) is an innovative high-frequency ground-penetrating radar (GPR) developed in the frame of the Martian NetLander mission to probe the subsurface down to kilometric depths. Unlike most GPRs, TAPIR is able to image underground reflectors with stationary antennas. In this paper, after a brief presentation of the instrument, we describe the method developed to interpret data collected during the RAdar of NEtlander in Terre Ade acutelie (RANETA) field survey in Antarctica. This method consists of retrieving the direction of arrival of each detected echo through the measurement of five components of the electromagnetic field (the three magnetic components and the horizontal components of the electric field). Thus, both the range and the direction of each individual reflection or diffraction due to the ice-bedrock interface are resolved. We validated this method on finite-difference time-domain numerically simulated data for different subsurface configurations before applying it to RANETA observations. In particular, the irregular topography of the bedrock in two sounding sites was revealed. We discuss the accuracy of our results. View full abstract»

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  • Infrared Thermography for Buried Landmine Detection: Inverse Problem Setting

    Publication Year: 2008 , Page(s): 3987 - 4004
    Cited by:  Papers (10)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2096 KB) |  | HTML iconHTML  

    This paper deals with an inverse problem arising in infrared (IR) thermography for buried landmine detection. It is aimed at using a thermal model and measured IR images to detect the presence of buried objects and characterize them in terms of thermal and geometrical properties. The inverse problem is mathematically stated as an optimization one using the well-known least-square approach. The main difficulty in solving this problem comes from the fact that it is severely ill posed due to lack of information in measured data. A two-step algorithm is proposed for solving it. The performance of the algorithm is illustrated using some simulated and real experimental data. The sensitivity of the proposed algorithm to various factors is analyzed. A data processing chain including anomaly detection and characterization is also introduced and discussed. View full abstract»

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  • On-Orbit Calibration Assessment of AVHRR Longwave Channels on MetOp-A Using IASI

    Publication Year: 2008 , Page(s): 4005 - 4013
    Cited by:  Papers (9)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1121 KB) |  | HTML iconHTML  

    Accurate and precise satellite radiance measurements are important for data assimilations in numerical weather prediction models and for climate-change detection. After the successful launch of the infrared atmospheric sounding interferometer (IASI), several studies have indicated that the IASI radiance measurements are well calibrated and maintain superb spectral and radiometric calibration accuracy. Owing to its hyperspectral nature and high-quality measurements, the IASI radiance can serve as a relative reference to independently assess the radiance measurements of broad- or narrow-band instruments that share the same spectral region. In this paper, we demonstrate the utility of the IASI radiances to evaluate the Advanced Very High Resolution Radiometer (AVHRR) infrared (IR) channel measurements. The coregistered AVHRR pixels inside each IASI pixel are averaged spatially. We then compared the spatially averaged radiance from AVHRR IR channels with IASI by convolving the IASI-measured spectra with the AVHRR spectral response functions. It was found that, statistically, the temperature observed from AVHRR channels 4 and 5 is slightly warmer than that in IASI for the brightness temperature (BT) range of 200 K-300 K. The mean BT difference (IASI minus AVHRR) is less than 0.4 K with a standard deviation of ~0.3 K for AVHRR channels 4 and 5. The BT difference between IASI and AVHRR IR channels is scene-temperature dependent for both channels 4 and 5, which is probably caused by the nonlinearity of the AVHRR detectors. Both AVHRR channels 4 and 5 show slight scan-dependent bias with maximum differences of approximately ~0.2 K (with AVHRR being warmer than IASI) at both ends of the scan. View full abstract»

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  • NOAA-AVHRR Orbital Drift Correction From Solar Zenithal Angle Data

    Publication Year: 2008 , Page(s): 4014 - 4019
    Cited by:  Papers (9)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (401 KB) |  | HTML iconHTML  

    This paper presents a new method for NOAA's (National Ocean and Atmospheric Administration) orbital drift correction. This method is pixel-based, and in opposition with most methods previously developed, does not need explicit knowledge of land cover. This method is applied to AVHRR (Advanced Very High Resolution Radiometer) channel information, and relies only on the additional knowledge of solar zenithal angle (SZA) and acquisition date information. In a first step, anomalies in SZA and channel time series are retrieved, and screened out for anomalous values. Then, the part of the parameter anomaly which is explained by SZA anomaly is removed from the data, to estimate new parameter anomalies, and this iteratively until the influence of SZA anomalies is totally removed from the parameter data. This correction has been applied to bimonthly AVHRR data provided by the GIMMS group (Global Inventory Modeling and Mapping Studies), covering Africa from November 2000 to December 2006. NDVI and LST (land surface temperature) have been estimated from raw and corrected data, and averaged over homogeneous vegetation classes. Differences between raw and corrected averaged parameters show an improvement in the quality of the data. In order to validate this method, a whole week (10 to 17 July 2004) of METEOSAT SEVIRI (Spinning Enhanced Visible and InfraRed Imager) data have been used, from which LST have been estimated using a similar method to the one used to retrieve LST from AVHRR data. The comparison between both platforms at the same time of acquisition shows good concordance. View full abstract»

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  • MRO/CRISM Retrieval of Surface Lambert Albedos for Multispectral Mapping of Mars With DISORT-Based Radiative Transfer Modeling: Phase 1—Using Historical Climatology for Temperatures, Aerosol Optical Depths, and Atmospheric Pressures

    Publication Year: 2008 , Page(s): 4020 - 4040
    Cited by:  Papers (12)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2467 KB) |  | HTML iconHTML  

    We discuss the DISORT-based radiative transfer pipeline (ldquoCRISM_LambertAlbrdquo) for atmospheric and thermal correction of MRO/CRISM data acquired in multispectral mapping mode (~200 m/pixel, 72 spectral channels). Currently, in this phase-one version of the system, we use aerosol optical depths, surface temperatures, and lower atmospheric temperatures, all from climatology derived from Mars Global Surveyor Thermal Emission Spectrometer (MGS-TES) data and from surface altimetry derived from MGS Mars Orbiter Laser Altimeter (MOLA). The DISORT-based model takes the dust and ice aerosol optical depths (scaled to the CRISM wavelength range), the surface pressures (computed from MOLA altimetry, MGS-TES lower atmospheric thermometry, and Viking-based pressure climatology), the surface temperatures, the reconstructed instrumental photometric angles, and the measured I/F spectrum as inputs, and then a Lambertian albedo spectrum is computed as the output. The Lambertian albedo spectrum is valuable geologically because it allows the mineralogical composition to be estimated. Here, I/F is defined as the ratio of the radiance measured by CRISM to the solar irradiance at Mars divided by pi; if there was no martian atmosphere, I/F divided by the cosine of the incidence angle would be equal to the Lambert albedo for a Lambertian surface. After discussing the capabilities and limitations of the pipeline software system, we demonstrate its application on several multispectral data cubes-particularly, the outer reaches of the northern ice cap of Mars, the Tyrrhena Terra area that is northeast of the Hellas basin, and an area near the landing site for the Phoenix mission in the northern plains. For the icy spectra near the northern polar cap, aerosols need to be included in order to properly correct for the CO2 absorption in the H2O ice bands at wavelengths near 2.0 mum. In future phases of software development, we intend to use CRISM data directly in order t- - o retrieve the spatiotemporal maps of aerosol optical depths, surface pressure, and surface temperature. This will allow a second level of refinement in the atmospheric and thermal correction of CRISM multispectral data. View full abstract»

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  • Scatterometer-Derived Soil Moisture Calibrated for Soil Texture With a One-Dimensional Water-Flow Model

    Publication Year: 2008 , Page(s): 4041 - 4049
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (527 KB) |  | HTML iconHTML  

    Current global satellite scatterometer-based soil moisture retrieval algorithms do not take soil characteristics into account. In this paper, the characteristic time length of the soil water index has been calibrated for ten sampling frequencies and for different soil conductivity associated with 12 soil texture classes. The calibration experiment was independently performed from satellite observations. The reference soil moisture data set was created with a 1-D water-flow model and by making use of precipitation measurements. The soil water index was simulated by applying the algorithm to the modeled soil moisture of the upper few centimeters. The resulting optimized characteristic time lengths T increase with longer sampling periods. For instance, a T of 7 days was found for sandy soil when a sampling period of 1 day was applied, whereas an optimized T-value of 18 days was found for a sampling period of 10 days. A maximum rmse improvement of 0.5% vol. can be expected when using the calibrated T-values instead of T = 20. The soil water index and the differentiated T-values were applied to European Remote Sensing (ERS) satellite scatterometer data and were validated against in situ soil moisture measurements. The results obtained using calibrated T -values and T = 20 did not differ ( r = 0.39, rmse = 5.4% vol.) and can be explained by the averaged sampling period of 4-5 days. The soil water index obtained with current operational microwave sensors [Advanced Wind Scatterometer (ASCAT) and Advanced Microwave Scanning Radiometer-Earth Observation System] and future sensors (Soil Moisture and Ocean Salinity and Soil Moisture Active Passive) should benefit from soil texture differentiation, as they can record on a daily basis either individually or synergistically using several sensors. The proposed differentiated characteristic time length enables the continuation of the soil water index of sen- - sors with varying sampling periods (e.g., ERS-ASCAT). View full abstract»

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  • Characterizing Bidimensional Roughness of Agricultural Soil Surfaces for SAR Modeling

    Publication Year: 2008 , Page(s): 4050 - 4061
    Cited by:  Papers (10)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1096 KB) |  | HTML iconHTML  

    In the description of agricultural soil roughness, the hypothesis of surface isotropy is currently admitted, and linear measurements are often used to characterize the soil roughness considered as a single-scale process. However, multiscale roughness is frequently observed, and tillage practices created oriented roughness. This paper presents a new technique to measure precisely the bidimensional soil roughness. Digital elevation model derived using photogrammetric technique reproduces the millimeter-scale height variations of three different soil surfaces (ploughed, smoothed, and row structured field) over about 8 m2 . A single surface measurement is sufficient to accurately measure the soil roughness parameters. Geostatistic parameterization allows the measurement of the roughness anisotropy. For smooth surface, a two-scale roughness is observed. Anisotropy is observed in the larger scale roughness. The proposed method allows the computation of the bidimensional correlation function, which is required by the integral equation method model for the simulation of the SAR signal over anisotropic soil surfaces. View full abstract»

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  • A Statistical Framework for the Sensitivity Analysis of Radiative Transfer Models

    Publication Year: 2008 , Page(s): 4062 - 4074
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (582 KB) |  | HTML iconHTML  

    Process models are widely used tools, both for studying fundamental processes themselves and as elements of larger system studies. A radiative transfer model (RTM) simulates the interaction of light with a medium. We are interested in RTMs that model light reflected from a vegetated region. Such an RTM takes as input various biospheric and illumination parameters and computes the upwelling radiation at the top of the canopy. The question we address is as follows: Which of the inputs to the RTM has the greatest impact on the computed observation? We study the leaf canopy model (LCM) RTM, which was designed to study the feasibility of observing leaf chemistry remotely. Its inputs are leaf chemistry variables (chlorophyll, water, lignin, and cellulose) and canopy structural parameters (leaf area index, leaf angle distribution, soil reflectance, and sun angle). We present a statistical approach to the sensitivity analysis of RTMs to answer the question previously posed. The focus is on global sensitivity analysis, studying how the RTM output changes as the inputs vary continuously according to a probability distribution over the input space. The influence of each input variable is captured through the ldquomain effectsrdquo and ldquosensitivity indices.rdquo Direct computation requires extensive computationally expensive runs of the RTM. We develop a Gaussian process approximation to the RTM output to enable efficient computation. We illustrate how the approach can effectively determine the inputs that are vital for accurate prediction. The methods are applied to the LCM with seven inputs and output obtained at eight wavelengths associated with Moderate-resolution Imaging Spectroradiometer bands that are sensitive to vegetation. View full abstract»

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  • A Statistical Method for Generating Cross-Mission Consistent Normalized Water-Leaving Radiances

    Publication Year: 2008 , Page(s): 4075 - 4093
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1828 KB) |  | HTML iconHTML  

    The accurate merging of primary radiometric ocean color products such as the normalized water-leaving radiance requires combining data from various space missions, which may be affected by different uncertainties as resulting from absolute calibration and minimization of the atmospheric effects. A statistical correction scheme based on a multilinear regression algorithm is used here to remove systematic differences between in situ and remote-sensing measurements. The application of the correction scheme to Sea-viewing Wide Field-of-View Sensor (SeaWiFS) and Moderate Resolution Imaging Spectroradiometer (MODIS) primary radiometric products improves the convergence between remote-sensing and in situ measurements, with the largest effects at 412 and 443 nm. Specifically, the scatter and bias of MODIS derived with respect to in situ L wn at 412 nm have shown values of 12% and 3% for corrected with respect to values of 34% and -28% for uncorrected data, respectively. Similarly, the scatter and bias for SeaWiFS-derived L wn at 412 nm have shown values of 14% and 4% for corrected with respect to 32% and -20% for uncorrected data. Results at 667 nm for MODIS and at 670 nm for SeaWiFS, although displaying a reduction in the scatter of data, have shown a significant residual bias of about 11% and 17% with respect to in situ values. Finally, it was shown the need for restricting the application of the correction scheme to data with atmospheric and marine optical features represented within the reference data set used to define the correction coefficients. View full abstract»

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  • Algorithm to Retrieve Aerosol Optical Properties From High-Spectral-Resolution Lidar and Polarization Mie-Scattering Lidar Measurements

    Publication Year: 2008 , Page(s): 4094 - 4103
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (841 KB) |  | HTML iconHTML  

    We developed an algorithm to estimate the vertical profiles of extinction coefficients at 532 nm for three aerosol types that are water-soluble, soot, and dust particles, using the extinction and backscattering coefficients at 532 nm for total aerosols derived from high-spectral-resolution lidar (HSRL) measurements and the receiving signal at 1064 nm and total depolarization ratio at 532 nm measured with Mie scattering lidar (MSL). The mode radii, standard deviations, and refractive indexes for each aerosol component are prescribed by the optical properties of aerosols and clouds database; the optical properties for each aerosol component are computed from Mie theory on the assumption that their particles are spherical and homogeneous, except for dust. To consider the effect of nonsphericity, the dust lidar ratio at 532 nm is assumed to be 50 sr, the value that is reported for Asian dust from the other observational studies. We performed sensitivity study on retrieval errors. The errors in extinction coefficient for each aerosol component were smaller than 30% and 60% when the measurement errors were plusmn5% and plusmn 10%. We demonstrated the ability of the algorithm by applying to the HSRL + MSL data measured at Tsukuba, Japan. Plumes consisting of water-soluble aerosols, soot, dust, or their mixture were retrieved; these results were consistent with simulation with a global aerosol transport model. Introducing the dust lidar ratio significantly improved a correlation between the retrieved dust concentration and the aerosol depolarization ratio at 532 nm derived from HSRL + MSL than the use of spherical dust optical model in the retrieval. View full abstract»

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  • A New Algorithm (ECICE) to Estimate Ice Concentration From Remote Sensing Observations: An Application to 85-GHz Passive Microwave Data

    Publication Year: 2008 , Page(s): 4104 - 4121
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3657 KB) |  | HTML iconHTML  

    A new algorithm, called Environment Canada's Ice Concentration Extractor (ECICE), has been developed to calculate total ice concentration and partial concentration of each ice type from remote-sensing observations. It employs two new concepts. First, it obtains a best estimate of ice concentrations by minimizing the sum of squared difference between observed and estimated radiometric values based on a linear radiometric model for each ice type. Second, instead of employing a single radiometric value (tie point) for each ice type, it utilizes the probability density distribution of the radiometric values for each ice type. Then, in a Monte Carlo simulation, 1000 radiometric values are randomly selected, total and ice-type concentrations are calculated by solving the minimization problem, and finally, median values from the 1000 simulations are chosen. The algorithm was applied to the winter sea ice in the Gulf of St. Lawrence, Canada, using observations from Special Sensor Microwave Imager (SSM/I) 85-GHz channel. Results were evaluated against ice concentration estimates from the operational analysis of Radarsat images at the Canadian Ice Service (CIS). Statistics of the differences between the output concentration and CIS estimates show that ECICE can successfully identify open water and consolidated pack ice pixels better than the Enhanced NASA Team algorithm. However, in areas of ice concentrations between 20% and 70%, the algorithm's performance could not be precisely evaluated because the typical size of the CIS's analysis polygon is much larger than the footprint of the 85-GHz SSM/I channel. Hence, the algorithm captures information at a finer spatial scale. Examples of using one, two, and three radiometric parameters to calculate the concentrations are presented. View full abstract»

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  • Striping Noise Detection and Correction of Remote Sensing Images

    Publication Year: 2008 , Page(s): 4122 - 4131
    Cited by:  Papers (10)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1758 KB) |  | HTML iconHTML  

    This paper presents an image destriping system for correcting striping noise of remote-sensing images. The developed system identifies stripe positions based on edge-detection and line-tracing algorithms. Pixels not affected by striping are used as control points to construct cubic spline functions describing spatial gray level distributions of an image. Detected stripes are corrected by replacing the pixels with more reasonable gray values computed from constructed spline functions. Gray values of clean pixels not affected by stripes are not altered to preserve data genuineness. Several experimental results demonstrate that the developed system can correctly detect stripes in remote-sensing images and effectively repair them. Evaluations of the results based on an quantitative image quality index indicate that the image quality has been improved significantly after destriping. The destriped images are not only visually more plausible but also can provide better interpretability and are more suitable for computerized analysis. View full abstract»

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  • Orthoimage Creation of Extremely High Buildings

    Publication Year: 2008 , Page(s): 4132 - 4141
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1683 KB) |  | HTML iconHTML  

    This paper presents a method for creating orthoimage in the urban area of extremely high buildings. The proposed method in this paper is different from the traditional methods, which improved the accuracy by increasing the number and/or improved the geometric distribution of ground control points. This proposed method first established a mathematical model of constraint condition on the building edges, such as perpendicularity, and then the established constraint conditions are merged into the orthorectification model. A test field located in downtown of Denver, CO, has been used to evaluate our methods. The experiments of comparing the accuracy achieved by our method and other methods are conducted. The experimental results demonstrated that the proposed method can improve the accuracy of 2-5 ft for those buildings of over 100 m high and even 5-7 ft for those buildings over 100 m high in the margin of imagery. View full abstract»

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  • Interpretation of Multisensor Remote Sensing Images: Multiapproach Fusion of Uncertain Information

    Publication Year: 2008 , Page(s): 4142 - 4152
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1671 KB) |  | HTML iconHTML  

    Land cover interpretation using multisensor remote sensing images is an important task that allows the extraction of information that is useful for several applications. However, satellite images are usually characterized by several types of imperfection, such as uncertainty, imprecision, and ignorance. Using additional sensors can help improve the image interpretation process and decrease the associated imperfections. Fusion methods such as the probability, possibility, and evidence methods can be used to combine information coming from these sensors. An extensive literature has accumulated during the last decade to resolve the issue of choosing the best fusion method, particularly for satellite images. In this paper, we present a semiautomatic approach based on case-based reasoning (CBR) and rule-based reasoning, allowing intelligent fusion method retrieval. This approach takes into account the advantage of data stored in the case base, allowing a more efficient processing and a decrease in image imperfections. The proposed approach incorporates three modules. The first is a learning module based on evaluating three fusion methods (probability, possibility, and evidence) applied to the given satellite images. The second looks for the best fusion method using CBR. The last is devoted to the fusion of multisensor images using the method retrieved by CBR. We validate our approach on a set of optical images coming from the Satellite Pour l'Observation de la Terre 4 and radar images coming from European Remote Sensing Satellite 2 (ERS-2) representing a central Tunisian region. View full abstract»

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  • Multiapproach System Based on Fusion of Multispectral Images for Land-Cover Classification

    Publication Year: 2008 , Page(s): 4153 - 4161
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1576 KB) |  | HTML iconHTML  

    Satellite image classification is usually marked by several types of imperfection such as uncertainty, imprecision, and ignorance. Data fusion of additional sensors tries to overcome the types of imperfection by using probability, possibility, and evidence theories. Our approach will lead to improve classification accuracy of satellite images by choosing the optimum theory for a particular image context and proposing a theoretical framework based on a multiagent system and case-based reasoning. We validate our approach trough a set of optical images from the satellite Satellite Positioning and Tracking 4 and radar images from the European Remote Sensing satellite 2, and we show that the overall accuracy is considerably increased from 83% for maximum-likelihood classification applied to multispectral imagery to 94% with the proposed approach. View full abstract»

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  • Assessing the Influence of Reference Spectra on Synthetic SAM Classification Results

    Publication Year: 2008 , Page(s): 4162 - 4172
    Cited by:  Papers (15)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (566 KB) |  | HTML iconHTML  

    Spectral matching algorithms, such as the Spectral Angle Mapper (SAM), utilize the spectral similarity between individual image pixel spectra and a spectral reference library with known components. Here, we illustrate and quantify the effects that different sources of reference libraries have on SAM classification results. Synthetic images of three mineral endmembers were classified by using reference libraries derived from airborne hyperspectral imagery, ground spectra (Portable Infrared Mineral Analyser), and from a standard library (United States Geologic Survey). Results show that the source of the reference library strongly influences the classification results if all available wavelengths are used. This effect can be partially neutralized by using appropriate preprocessing methods. Two different types of spectral subsetting of the data, two types of continuum removal, and a combination thereof were tested. Best results were achieved by using a feature subset (i.e., limiting the input wavelengths to the diagnostic absorption features). This increased the average classification accuracy from 74% to 95% (ground spectral library) and from 68% to 94% (standard library). View full abstract»

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  • An Adaptive Mean-Shift Analysis Approach for Object Extraction and Classification From Urban Hyperspectral Imagery

    Publication Year: 2008 , Page(s): 4173 - 4185
    Cited by:  Papers (66)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1747 KB) |  | HTML iconHTML  

    In this paper, an adaptive mean-shift (MS) analysis framework is proposed for object extraction and classification of hyperspectral imagery over urban areas. The basic idea is to apply an MS to obtain an object-oriented representation of hyperspectral data and then use support vector machine to interpret the feature set. In order to employ MS for hyperspectral data effectively, a feature-extraction algorithm, nonnegative matrix factorization, is utilized to reduce the high-dimensional feature space. Furthermore, two bandwidth-selection algorithms are proposed for the MS procedure. One is based on the local structures, and the other exploits separability analysis. Experiments are conducted on two hyperspectral data sets, the DC Mall hyperspectral digital-imagery collection experiment and the Purdue campus hyperspectral mapper images. We evaluate and compare the proposed approach with the well-known commercial software eCognition (object-based analysis approach) and an effective spectral/spatial classifier for hyperspectral data, namely, the derivative of the morphological profile. Experimental results show that the proposed MS-based analysis system is robust and obviously outperforms the other methods. View full abstract»

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  • Learning Sparse CRFs for Feature Selection and Classification of Hyperspectral Imagery

    Publication Year: 2008 , Page(s): 4186 - 4197
    Cited by:  Papers (17)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (711 KB) |  | HTML iconHTML  

    Feature selection is an important task in hyperspectral data analysis. This paper presents a sparse conditional random field (SCRF) model to select relevant features for the classification of hyperspectral images and, meanwhile, to exploit the contextual information in the form of spatial dependences in the images. The sparsity arises from the use of a Laplacian prior on the CRF parameters, which encourages the parameter estimates to be either significantly large or exactly zero. To joint the feature selection and classifier design, this paper develops an efficient sparse training method, which divides the training of SCRF into the sparse trainings of two simpler classifiers. Experiments on the real-world hyperspectral image attest to the accuracy, sparsity, and efficiency of the proposed model. View full abstract»

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  • An Innovative Method to Classify Remote-Sensing Images Using Ant Colony Optimization

    Publication Year: 2008 , Page(s): 4198 - 4208
    Cited by:  Papers (10)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1357 KB) |  | HTML iconHTML  

    This paper presents a new method to improve the classification performance for remote-sensing applications based on swarm intelligence. Traditional statistical classifiers have limitations in solving complex classification problems because of their strict assumptions. For example, data correlation between bands of remote-sensing imagery has caused problems in generating satisfactory classification using statistical methods. In this paper, ant colony optimization (ACO), based upon swarm intelligence, is used to improve the classification performance. Due to the positive feedback mechanism, ACO takes into account the correlation between attribute variables, thus avoiding issues related to band correlation. A discretization technique is incorporated in this ACO method so that classification rules can be induced from large data sets of remote-sensing images. Experiments of this ACO algorithm in the Guangzhou area reveal that it yields simpler rule sets and better accuracy than the See 5.0 decision tree method. View full abstract»

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  • 2008 Index IEEE Transactions on Geoscience and Remote Sensing Vol. 46

    Publication Year: 2008 , Page(s): 4209 - 4260
    Save to Project icon | Request Permissions | PDF file iconPDF (533 KB)  
    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.

 

Full Aims & Scope

Meet Our Editors

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