By Topic

Geoscience and Remote Sensing Letters, IEEE

Issue 4 • Date Oct. 2006

Filter Results

Displaying Results 1 - 25 of 37
  • [Front cover]

    Publication Year: 2006 , Page(s): c1
    Save to Project icon | Request Permissions | PDF file iconPDF (838 KB)  
    Freely Available from IEEE
  • IEEE Geoscience and Remote Sensing Letters publication information

    Publication Year: 2006 , Page(s): c2
    Save to Project icon | Request Permissions | PDF file iconPDF (38 KB)  
    Freely Available from IEEE
  • Table of contents

    Publication Year: 2006 , Page(s): 437 - 438
    Save to Project icon | Request Permissions | PDF file iconPDF (49 KB)  
    Freely Available from IEEE
  • Neuro-Fuzzy Prediction-Based Adaptive Filtering Applied to Severely Distorted Magnetic Field Recordings

    Publication Year: 2006 , Page(s): 439 - 441
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (154 KB) |  | HTML iconHTML  

    This letter presents an adaptive filtering technique, based upon neuro-fuzzy prediction, to enhance magnetic field signal recordings affected by significant anomalies of magnetotelluric origin such as magnetic storms, rain, and cultural noise. A neuro-fuzzy model has been developed and trained to predict the magnetic field signal in the absence of any sizeable disturbances. Thus, at the occurrence of a significant distortion of nonmagnetotelluric origin, the neuro-fuzzy model predicts the healthy magnetic field signal in parallel to the distortion, thereby significantly reducing the latter. Testing the trained system using unseen data verifies the reliability of the model and demonstrates the effectiveness of the neuro-fuzzy prediction-based adaptive filtering method View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • On the Characterization of Buried Targets Under a Rough Surface Using the Wigner–Ville Transformation

    Publication Year: 2006 , Page(s): 442 - 446
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (225 KB)  

    This letter considers the problem of detecting and characterizing a target buried beneath a rough surface separating two homogeneous half spaces. The problem of detecting the target is tackled by analyzing the frequency-averaged Wigner-Ville function, the purpose of which is to filter out rough surface scattering. Characterization of the target is performed using the iterative solution derived from the Newton-Kantorovitch algorithm as applied to the Wigner-Ville function instead of the scattered field as is usually done. In addition, the scattering model involved in the inversion scheme assumes a flat interface, and surface roughness is handled as clutter. The efficiency of the approach is illustrated through numerical experiments, and the comparison between inversions from the scattered field and the Wigner-Ville function is reported View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A Closed-Form Expression of the Electromagnetic Tensor Green's Functions for a Homogeneous TI-Anisotropic Medium

    Publication Year: 2006 , Page(s): 447 - 451
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (104 KB) |  | HTML iconHTML  

    In this letter, the electromagnetic fields from electric and magnetic current source distributions located in an unbounded homogeneous transverse isotropic (TI)-anisotropic medium are determined. The expressions provide the closed-form tensor Green's functions needed in any integral equation formulation of the forward as well as the inverse scattering problems associated with scatterers embedded in a homogeneous TI-anisotropic medium View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • The Global Impact of Clouds on the Production of MODIS Bidirectional Reflectance Model-Based Composites for Terrestrial Monitoring

    Publication Year: 2006 , Page(s): 452 - 456
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (252 KB) |  | HTML iconHTML  

    A global data set of cloud occurrence probability derived from Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua gridded daily data is analyzed to investigate the probability of obtaining at least a minimum number of cloud-free observations within various compositing periods. The probabilities derived from Terra and Aqua, with morning and afternoon overpass times, respectively, are similar and increase with compositing period. Compositing both Terra and Aqua observations results in considerably higher probabilities of obtaining a sufficient number of observations for bidirectional reflectance model-based compositing. Given that the only alternative to obtaining sufficient samples is to extend the observation period, which can cause significant problems when the surface state changes, it is concluded that using data from the two MODIS sensors provides the most effective way of generating composited products. Findings with respect to the availability of cloud-free composites when n-day composites are generated on a temporally overlapping daily rolling basis, i.e., every day, rather than every n-days, are also discussed for regional and global applications View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Markovian Fusion Approach to Robust Unsupervised Change Detection in Remotely Sensed Imagery

    Publication Year: 2006 , Page(s): 457 - 461
    Cited by:  Papers (17)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (627 KB) |  | HTML iconHTML  

    The most common methodology to carry out an automatic unsupervised change detection in remotely sensed imagery is to find the best global threshold in the histogram of the so-called difference image. The unsupervised nature of the change detection process, however, makes it nontrivial to find the most appropriate thresholding algorithm for a given difference image, because the best global threshold depends on its statistical peculiarities, which are often unknown. In this letter, a solution to this issue based on the fusion of an ensemble of different thresholding algorithms through a Markov random field framework is proposed. Experiments conducted on a set of five real remote sensing images acquired by different sensors and referring to different kinds of changes show the high robustness of the proposed unsupervised change detection approach View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Empirical Estimation of Nearshore Waves From a Global Deep-Water Wave Model

    Publication Year: 2006 , Page(s): 462 - 466
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (357 KB) |  | HTML iconHTML  

    Global wind-wave models such as the National Oceanic and Atmospheric Administration WaveWatch 3 (NWW3) play an important role in monitoring the world's oceans. However, untransformed data at grid points in deep water provide a poor estimate of swell characteristics at nearshore locations, which are often of significant scientific, engineering, and public interest. Explicit wave modeling, such as the Simulating Waves Nearshore (SWAN), is one method for resolving the complex wave transformations affected by bathymetry, winds, and other local factors. However, obtaining accurate bathymetry and determining parameters for such models is often difficult. When target data is available (i.e., from in situ buoys or human observers), empirical alternatives such as artificial neural networks (ANNs) and linear regression may be considered for inferring nearshore conditions from offshore model output. Using a sixfold cross-validation scheme, significant wave height Hs and period were estimated at one onshore and two nearshore locations. In estimating Hs at the shoreline, the validation performance of the best ANN was r=0.91, as compared to those of linear regression (0.82), SWAN (0.78), and the NWW3 Hs baseline (0.54) View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Estimation of Sound Speed Profiles Using Artificial Neural Networks

    Publication Year: 2006 , Page(s): 467 - 470
    Cited by:  Papers (6)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (154 KB) |  | HTML iconHTML  

    The vast and complex oceans that are optically opaque are acoustically transparent, enabling characterization of physical and biological bodies and processes of sea using sound as a premier tool. Lack of direct observations of vertical profiles of velocimeters and/or temperature and salinity, from which sound speed can be calculated, limits specifications and investigation of temporal and spatial variabilities of the three-dimensional structure of the sound speed in the oceans. In this study, the authors demonstrate estimation of sound speed profiles (SSPs) from surface observations using an artificial neural network (ANN) method. Surface observations from a mooring in the central Arabian Sea are used as a proxy to the satellite observations. The ANN-estimated SSPs had a root-mean-square error of 1.16 m/s and a coefficient of determination of 0.98. About 76% (93%) of the estimates lie within plusmn1 m/s (plusmn2 m/s) of the SSPs obtained from in situ temperature and salinity profiles View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Internal Layer Tracing and Age–Depth Relationship From the Ice Divide Toward Jakobshavn, Greenland

    Publication Year: 2006 , Page(s): 471 - 475
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1003 KB)  

    Signs of long-term glaciological processes and past ice-sheet structure are preserved in the internal layer manifestations of the Greenland ice sheet. Internal layer data have been collected over a substantial portion of the Greenland ice sheet using the University of Kansas ice-sounding radar. In this letter, these layers are traced along thousands of kilometers of flight lines from the ice divide toward Jakobshavn, which is the most active glacier in Greenland. The authors determine the traced radar layers' age at the Greenland Ice Core Project (GRIP) site using the GRIP core age-depth relationship. Inasmuch as the depth varies spatially for a layer of a specific dated age, an age-depth relationship for each location along the flight lines of this letter can be found using the traced layers. Thirty-one points where flight lines cross over one another were analyzed. From the flight line crossover analysis, a 9-m maximum difference (< 1%) was found View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Nonstationary Spatial Texture Estimation Applied to Adaptive Speckle Reduction of SAR Data

    Publication Year: 2006 , Page(s): 476 - 480
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1726 KB)  

    This letter proposes a new model for the second-order statistics of spatial texture in synthetic aperture radar images. The autocovariance function is locally approximated by a two-dimensional anisotropic Gaussian kernel (AGK) to characterize texture by its local orientation and anisotropy. The estimation of texture parameters at a given scale is based on the gradient structure tensor operator and does not require the explicit computation of the autocovariance. Finally, a new filter called AGK minimum mean square error (MMSE) that takes into account this spatial information is introduced and compared with the refined MMSE filter. The proposed filter has better performance in terms of texture preservation and structure enhancement View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Estimating Interannual Variations in Vegetated Areas of Sardinia Island Using SPOT/VEGETATION NDVI Temporal Series

    Publication Year: 2006 , Page(s): 481 - 483
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (434 KB)  

    Principal component analysis (PCA) has been applied to a temporal series 1999-2002 of a yearly maximum value composite of the SPOT/VEGETATION normalized difference vegetation index for the Sardinia Island for extracting interannual variations affecting vegetation covers. Both naturally vegetated areas (forest, shrub-land, and herbaceous cover) and agricultural lands have been investigated in order to obtain information on the most prominent natural and/or man-induced alterations affecting vegetation behavior. Although a correct interpretation of PCA results generally requires additional information, such as geographical knowledge, climatological data, and field surveys, the main finding of the current investigation suggests that PCA can be a feasible tool to separately map areas showing different degrees of interannual variability, providing valuable information for discriminating unidirectional changes View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Asian Dust Storm Monitoring Combining Terra and Aqua MODIS SRB Measurements

    Publication Year: 2006 , Page(s): 484 - 486
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1934 KB) |  | HTML iconHTML  

    Sand and dust storms (SDSs), which present environmental risks and affect the regional climate, have been worsened in the East Asian regions over the last decade. Monitoring SDS from space using satellite remote sensing (RS) has become one of the most important issues in this field. At present, satellite RS of SDS is limited to using true-color images or aerosol optical thickness (AOT), or a new algorithm called "Deep Blue". Using current existing approaches makes it difficult to identify SDS from clouds. The authors have detected SDS by combining Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) solar reflectance band (SRB) measurements. Based on the dust spectral characteristic, this letter proposes a normalized difference dust index (NDDI) using MODIS reflectance measurements and applies it to the Asian SDS cases. The simple NDDI index is found to be able to identify SDS and clouds easily. The results suggest that NDDI could be used to detect SDS over bright surfaces where the MODIS AOT product is not available View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • The Exact Distribution of the Multilook Magnitude

    Publication Year: 2006 , Page(s): 487 - 490
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (94 KB) |  | HTML iconHTML  

    Gierull provides a statistical analysis of multilook synthetic aperture radar interferograms. Various expressions for the probability density function, cumulative distribution function, and the moments of associated statistics are derived. It appears, however, that most of these expressions are based on some approximation. In this letter, the corresponding expressions are derived in their exact form, including some elementary representations for certain expressions given by Gierull. A numerical comparison of the exact and approximate expressions is provided View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Logistic Regression for Feature Selection and Soft Classification of Remote Sensing Data

    Publication Year: 2006 , Page(s): 491 - 494
    Cited by:  Papers (15)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (127 KB) |  | HTML iconHTML  

    Feature selection is a key task in remote sensing data processing, particularly in case of classification from hyperspectral images. A logistic regression (LR) model may be used to predict the probabilities of the classes on the basis of the input features, after ranking them according to their relative importance. In this letter, the LR model is applied for both the feature selection and the classification of remotely sensed images, where more informative soft classifications are produced naturally. The results indicate that, with fewer restrictive assumptions, the LR model is able to reduce the features substantially without any significant decrease in the classification accuracy of both the soft and hard classifications View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • In Search of the Statistical Properties of High-Resolution Polarimetric SAR Data for the Measurements of Forest Biomass Beyond the RCS Saturation Limits

    Publication Year: 2006 , Page(s): 495 - 499
    Cited by:  Papers (8)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (357 KB) |  | HTML iconHTML  

    The purpose of this letter is to present the results on the study of searching effective parameters that describe the relation between high-resolution synthetic aperture radar (SAR) images and forest parameters. The study is based on the non-Gaussian texture analysis of the polarimetric airborne Pi-SAR data over coniferous forests in Hokkaido, Japan. The radar cross section (RCS) in terms of a forest biomass is first analyzed. It is found that the L-band RCS increases steadily with the biomass and saturates at approximately 40 tons/ha. These results are similar to the previous studies. The probability density function of the image amplitude is then investigated, and among Rayleigh, log-normal, Weibull, and K-distributions, the K-distribution is found to fit best to the L-band data of all polarizations, although the Weibull distribution fits equally well. Further, the correlation between the tree biomass and the order parameter of the K-distribution in the cross-polarization images is found to be very high, and the order parameter increases consistently with the biomass to approximately 100 tons/ha, which is well beyond the saturation limit of the L-band RCS. Thus, the order parameter of the K-distribution can be a promising new parameter to estimate the forest biomass from high-resolution polarimetric SAR data in a much wider range than the conventional RCS method View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • ELF Radar System Proposed for Localized D-Region Ionospheric Anomalies

    Publication Year: 2006 , Page(s): 500 - 503
    Cited by:  Papers (11)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (359 KB) |  | HTML iconHTML  

    This letter proposes a novel extremely low frequency (ELF) radar for localized D-region (altitude < 95 km) ionospheric anomalies that have been generated by natural geophysical processes. The proposed system would use the former U.S. Navy Wisconsin Transmitting Facility as a distant well-characterized impulsive ELF source. Sample calculations that demonstrate how passive vertical E-field detectors could characterize ionospheric conductivity depressions of variable diameter located above Los Angeles are provided. These calculations have been obtained using our recently developed three-dimensional whole-Earth electromagnetic wave propagation model based upon the rigorous finite-difference time-domain solution of Maxwell's equations. A key potential application of the proposed ELF radar system is the detection of hypothesized ionospheric earthquake precursors View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • An Adaptive and Iterative Method of Urban Area Extraction From SAR Images

    Publication Year: 2006 , Page(s): 504 - 507
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (539 KB) |  | HTML iconHTML  

    This letter presents a new method for unsupervised urban area extraction from synthetic aperture radar (SAR) images based on the ffmax algorithm proposed by C. Gouinaud specially for acquiring urban areas in SPOT imagery. According to the statistical characteristics of urban areas, an adaptive and iterative method based on the low-level extraction given by the ffmax algorithm using a large window is proposed. Experimental results on real SAR images show that the proposed automatic method works quickly and can preserve the borders of urban areas as well as avoid the disturbance of other classes and the extractions of urban areas are reliable and precise View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • The Potential of the Maximum Cross-Correlation Technique to Estimate Surface Currents From Thermal AVHRR Global Area Coverage Data

    Publication Year: 2006 , Page(s): 508 - 511
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (481 KB) |  | HTML iconHTML  

    Having already shown its potential of deriving the vector fields representing the ocean-surface advection from sequential 1.1-km-resolution local area coverage (LAC) Advanced Very High Resolution Radiometer (AVHRR) images, the maximum cross-correlation (MCC) technique here is applied to four 4.4-km-resolution global area coverage (GAC) AVHRR images. The resulting three vector fields are compared to the vector fields obtained from the LAC imagery corresponding to the same satellite passages. To quantify the reduction in accuracy inevitable when applying the method to the lower resolution imagery, the LAC vector fields were assumed to be error free. The deviation of the GAC vectors from the LAC vectors is expressed as percentage errors of the signal variance of meridional u and zonal v velocity components, and they are 16%/30%, respectively, for the best case and 62%/117% and 92%/111% for the other two cases. These results indicate that, in its present state, the GAC data do not allow the MCC technique to extract reliable current-vector information from it View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • SAR Minimum-Entropy Autofocus Using an Adaptive-Order Polynomial Model

    Publication Year: 2006 , Page(s): 512 - 516
    Cited by:  Papers (21)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1317 KB) |  | HTML iconHTML  

    A new algorithm is presented for autofocus in synthetic aperture radar imaging. Entropy is used to measure the focus quality of the image, and better focus corresponds to smaller entropy. The phase response of the focus filter is modeled as a specially designed polynomial, and the coefficients of this polynomial are adjusted in sequence to minimize the entropy of the image. Because the order of this polynomial is adaptive, this algorithm applies more widely than the minimum-entropy algorithms with a fixed-order polynomial model View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Supervised SAR Image MPM Segmentation Based on Region-Based Hierarchical Model

    Publication Year: 2006 , Page(s): 517 - 521
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (801 KB) |  | HTML iconHTML  

    This letter presents a novel method of supervised multiresolution segmentation for synthetic aperture radar images. The method uses a region-based half-tree hierarchical Markov random field model for multiresolution segmentation. To form the region-based multilayer model, the watershed algorithm is employed at each resolution level independently. The nodes of a quadtree in the proposed model are defined as regions instead of pixels. The relationship over scale is studied, and the region-based upward and downward maximization of posterior marginal estimations are deduced. The experimental results for the segmentation of homogeneous areas prove the region-based model much better in terms of robustness to speckle and preservation of edges compared to the pixel-based hierarchical model and the Gibbs sampler with the single-resolution model View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Band Selection for Hyperspectral Image Classification Using Mutual Information

    Publication Year: 2006 , Page(s): 522 - 526
    Cited by:  Papers (48)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (373 KB) |  | HTML iconHTML  

    Spectral band selection is a fundamental problem in hyperspectral data processing. In this letter, a new band-selection method based on mutual information (MI) is proposed. MI measures the statistical dependence between two random variables and can therefore be used to evaluate the relative utility of each band to classification. A new strategy is described to estimate the MI using a priori knowledge of the scene, reducing reliance on a "ground truth" reference map, by retaining bands with high associated MI values (subject to the so-called "complementary" conditions). Simulations of classification performance on 16 classes of vegetation from the AVIRIS 92AV3C data set show the effectiveness of the method, which outperforms an MI-based method using the associated reference map, an entropy-based method, and a correlation-based method. It is also competitive with the steepest ascent algorithm at much lower computational cost View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Unsupervised Segmentation of Hyperspectral Images Using Modified Phase Correlation

    Publication Year: 2006 , Page(s): 527 - 531
    Cited by:  Papers (10)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (495 KB) |  | HTML iconHTML  

    This letter presents hyperspectral image segmentation based on the phase-correlation measure of subsampled hyperspectral data, which is referred to as modified phase correlation. The hyperspectral spectrum of each pixel is initially subsampled to gain robustness against noise and spatial variability, and phase correlation is applied to determine spectral similarity. Similar and dissimilar pixels are decided according to the peak value of the phase correlation result to determine pixels that fall into the same segments. The approach can be regarded as a region-growing technique. The total number of segments is determined automatically according to the similarity threshold View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Automated Allocation of Highly Structured Urban Areas in Homogeneous Zones From Remote Sensing Data by Savitzky&#8211;Golay Filtering and Curve Sketching

    Publication Year: 2006 , Page(s): 532 - 536
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (909 KB) |  | HTML iconHTML  

    City morphology not only reveals spatial distribution of diverse physical parameters but also of diverse socio-economic characteristics. Because of this, spatial structure or zoning in urban spaces is a key variable for inferring information valuable for assessment, planning, and management purposes. The presented methodology shows a mathematical approach to derive homogeneous zones from a solely remote sensing land-cover classification result. By Savitzky-Golay filtering and a subsequent curve-sketching approach, an interpreter-independent differentiation within a city is computed. The classification shows the result of the arrangement of urban zoning without any ancillary data View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.

Aims & Scope

IEEE Geoscience and Remote Sensing Letters (GRSL) is a monthly publication for short papers (maximum length 5 pages) addressing new ideas and formative concepts in remote sensing as well as important new and timely results and concepts.

 

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Alejandro C. Frery
Universidade Federal de Alagoas