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Geoscience and Remote Sensing Letters, IEEE

Issue 1 • Date Jan. 2013

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  • [Front cover]

    Page(s): C1
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  • IEEE Geoscience and Remote Sensing Letters publication information

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  • Table of contents

    Page(s): 1 - 3
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  • Composite Scattering of a Plasma-Coated Target Above Dispersive Sea Surface by the ADE-FDTD Method

    Page(s): 4 - 8
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (229 KB) |  | HTML iconHTML  

    In this letter, the composite scattering of a plasma-coated target above a randomly rough sea surface is investigated by the finite-difference time-domain (FDTD) method with pulsed wave excitation. Both sea water and plasma layer are regarded as isotropic and dispersive media, and they satisfy the Debye model and the Drude model, respectively. The auxiliary differential equation (ADE) technique is incorporated into the FDTD scheme to deal with the dispersive media. To ensure the feasibility of our present method, the scattering of a perfect electrically conducting target without coating above dispersive sea surface by the ADE-FDTD method is compared with the result obtained by the parallel method of moments, which requires an individual run for every frequency of interest. Finally, the normalized radar cross section of a plasma-coated target above rough sea surface is calculated and analyzed for different parameters in detail. View full abstract»

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  • The Potential of COSMO-SkyMed SAR Images in Monitoring Snow Cover Characteristics

    Page(s): 9 - 13
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (1151 KB) |  | HTML iconHTML  

    Monitoring of snow cover is crucial to the study of global climate changes for water resource management, as well as for flood and avalanche risk prevention. The sensitivity to snow characteristics of X-band backscattering of COSMO-SkyMed mission has been analyzed in the framework of experimental and model activities. X-band data have been found to contribute to the retrieval of the snow water equivalent (SWE), provided that the snow cover is characterized by a snow depth (SD) of roughly 60-70 cm (SWE >; 100-150 mm) and with relatively large crystal dimensions. Subsequently, an algorithm for retrieving SD or SWE has been developed and tested with experimental data collected on several ground stations. View full abstract»

    Open Access
  • A Novel SAR Image Change Detection Based on Graph-Cut and Generalized Gaussian Model

    Page(s): 14 - 18
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (608 KB) |  | HTML iconHTML  

    In this letter, a robust and fast unsupervised change-detection framework is proposed for synthetic aperture radar (SAR) images. It contains three aspects. First, a robust difference image is constructed with the idea of probability patch-based, and it can suppress the speckle effects on the changed regions and enhance the change information synchronously. Then, each class of the difference image is modeled by generalized Gaussian distribution (GGD), and its parameters are learned by the expectation-maximization algorithm. Moreover, the graph-cut algorithm is employed on the difference image to extract the spatial prior information, based on which the parameters of GGD are initialized well via the fuzzy c-means algorithm. Finally, the Bayesian inference for maximum a posteriori performs the final detection. Experimental results on simulated and real SAR data sets confirm the robustness and accuracy of the proposed algorithm in which graph-cut and GGD make great contribution on improving the accuracy of detection and speed of algorithm. View full abstract»

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  • Decision-Based Fusion for Pansharpening of Remote Sensing Images

    Page(s): 19 - 23
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (534 KB) |  | HTML iconHTML  

    Pansharpening may be defined as the process of synthesizing multispectral images at a higher spatial resolution. A wide range of pansharpening methods are available, each producing images with different characteristics. To compare the performances and characteristics of different methods, a contest was held in 2006 by the IEEE Data Fusion Technical Committee. In this contest, À trous wavelet transform-based pansharpening (AWLP) and Laplacian pyramid-based context adaptive (CBD) pansharpening methods were declared as joint winners. While assessing the quantitative quality of the pansharpened images, we observed that the two methods outperform each other depending upon the local content of the scene. Hence, it is interesting to design a method taking advantage of both methods by locally selecting the best one. This adaptive decision fusion is performed based on the local scale of the structure. The interest of the proposed method is verified using both visual and quantitative analyses for different Pléiades data sets. View full abstract»

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  • Empirical Automatic Estimation of the Number of Endmembers in Hyperspectral Images

    Page(s): 24 - 28
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (557 KB) |  | HTML iconHTML  

    In this letter, an eigenvalue-based empirical method is proposed in order to estimate the number of endmembers in hyperspectral data. This method is based on the distribution of the differences of the eigenvalues from the correlation and the covariance matrices, respectively. The eigenvalues corresponding to the noise are identical in the covariance and the correlation matrices, while the eigenvalues corresponding to the signal (the endmembers) are larger in the correlation matrix than in the covariance matrix. The proposed method is totally parameter free and very fast. It is validated by experiments carried on both synthetic and real data sets. View full abstract»

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  • Discriminative Gabor Feature Selection for Hyperspectral Image Classification

    Page(s): 29 - 33
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (180 KB) |  | HTML iconHTML  

    Three-dimensional Gabor wavelets have recently been successfully applied for hyperspectral image classification due to their ability to extract joint spatial and spectrum information. However, the dimension of the extracted Gabor feature is incredibly huge. In this letter, we propose a symmetrical-uncertainty-based and Markov-blanket-based approach to select informative and nonredundant Gabor features for hyperspectral image classification. The extracted Gabor features with large dimension are first ranked by their information contained for classification and then added one by one after investigating the redundancy with already selected features. The proposed approach was fully tested on the widely used Indian Pine site data. The results show that the selected features are much more efficient and can achieve similar performance with previous approach using only hundreds of features. View full abstract»

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  • A Neural Network Approach to Improve the Vertical Resolution of Atmospheric Temperature Profiles From Geostationary Satellites

    Page(s): 34 - 37
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (329 KB) |  | HTML iconHTML  

    Tropospheric temperature measurements at high temporal, spatial, and vertical resolutions are required for many meteorological studies. Radiosonde and Global Positioning System radio occultation (GPSRO) observations have very high vertical resolutions but poor in spatial and temporal coverage. Although the sounders on geostationary satellites can provide high temporal and spatial resolutions, their vertical resolution is poor. In this letter, we proposed a method to increase the vertical resolution of tropospheric temperature profiles obtained from geostationary satellite observations based on an artificial neural network (ANN) approach so that high-resolution temperature profiles are available in all four dimensions. We simulated the pressure levels of the forthcoming Indian National Satellite System (INSAT) 3-D temperature measurements from 950 to 100 hPa using 1-D variational temperature profiles of the Constellation Observing System for Meteorology Ionosphere and Climate (COSMIC). We used these low-resolution simulated profiles as the predictors and the high-resolution GPSRO COSMIC profiles as predictants. The data during 2007 and 2008 were used to develop the model, and the data during 2009 were used for validation. The correlation coefficient of greater than 0.94 is observed throughout the pressure levels for all the three data sets. The root-mean-square differences of training, selection, and validation sets are 0.43, 0.46, and 0.51, respectively. A scatter index of less than 0.002 for all the three data sets indicates the accuracy of the estimations. View full abstract»

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  • Individual Deciduous Tree Recognition in Leaf-Off Aerial Ultrahigh Spatial Resolution Remotely Sensed Imagery

    Page(s): 38 - 42
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (503 KB) |  | HTML iconHTML  

    This study proposed and tested a multistep method for the recognition of individual deciduous trees in leaf-off aerial ultrahigh spatial resolution remotely sensed (UHSRRS) imagery. This topic has received limited coverage in previous endeavors, which focused mainly on the detection and delineation of coniferous trees in remotely sensed images with relatively lower spatial resolutions. Thus, the traditional algorithms tend to fail in case of the referred scenario. In order to fill this technical gap, an algorithm that joins mathematical morphological operations and marker-controlled watershed segmentation was first assumed for the extraction of single trees in UHSRRS images. Next, a distribution-free support vector machine (SVM) classifier was applied to distinguish the extracted segments as deciduous or coniferous trees, merely in terms of two newly-derived morphological features. Experimental evaluations indicated that the integral solution plan can extract and classify the deciduous and coniferous trees in the leaf-off aerial UHSRRS images of local dense forest for test with correctness over 92% and 70%, respectively. Overall, the recognition results with >;66% correctness have primarily validated the proposed technique. View full abstract»

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  • Wavelet-Based Rapid Estimation of Earthquake Magnitude Oriented to Early Warning

    Page(s): 43 - 47
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (388 KB) |  | HTML iconHTML  

    The main goal of an earthquake early warning system (EEWS) is to estimate the magnitude of an underway rupture from the first few seconds in order to allow hazard assessment and mitigation before destructive events occur. This letter investigates the application of a wavelet-based algorithm for local magnitude estimation in the South Aegean Sea (focusing on Crete Island) which is covered by a sparse seismological network. A relation between the first few seconds of the first-arriving energy at the surface, the P wave, and the local magnitude of the earthquake has been developed for the area of interest. Results show that the errors produced by the proposed method present less scattering than relevant magnitude rapid estimation methods. It is the first time that such a method is applied in a sparse seismological network since all the previous studies took place in high-density networks. This fact expands the applicability of EEWS and also provides an alternative magnitude estimator for the currently developed EEWS. View full abstract»

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  • DEM-Based SAR Pixel-Area Estimation for Enhanced Geocoding Refinement and Radiometric Normalization

    Page(s): 48 - 52
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1147 KB) |  | HTML iconHTML  

    Precise terrain-corrected georeferencing of synthetic aperture radar (SAR) images and derived products in range–Doppler coordinates is important with respect to several aspects, such as data interpretation, combination with other geodata products, and transformation of, e.g., terrain heights into SAR geometry as used in differential interferometric SAR (DInSAR) applications. For georeferencing, a lookup table is calculated and then refined based on a coregistration of the actual SAR image to a simulated SAR image. The impact of using two different implementations of such a simulator of topography-induced radar brightness, 1) an approach based on angular relationships and 2) a pixel-area-based method, is discussed in this letter. It is found that the pixel-area-based method leads to considerable improvements with regard to the robustness of georeferencing and also with regard to radiometric normalization in layover-affected areas. View full abstract»

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  • Virtual-Echo Projection Method for Suppression of Noise in UWB-SAR Imaging Systems

    Page(s): 53 - 56
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (510 KB) |  | HTML iconHTML  

    The conventional time-domain back-projection (BP) imaging method typically suffers from performance degradation when the echo signals are interfered by noise. In this letter, a virtual-echo projection imaging method is proposed for noise suppression, including the suppression of the additive white Gaussian noise and the irrelevant colored noise. First, the proposed method calculates a number of coarse imaging maps by projecting the realistic echoes on the virtual echoes. Second, the fine imaging maps are calculated by accumulating all the coarse imaging maps coherently. The performance of the proposed method is numerically investigated in comparison to that of the traditional BP method. The results indicate that the proposed method outperforms the conventional BP method in suppressing both the additive white Gaussian noise and the colored noise. View full abstract»

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  • Delay Tracking in Spaceborne GNSS-R Ocean Altimetry

    Page(s): 57 - 61
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (343 KB) |  | HTML iconHTML  

    Tracking, in radar altimetry, is the positioning of the waveforms in the correlation window. This letter presents a tracking strategy in spaceborne altimetry using global navigation satellite system reflectometry. First, the tracking procedure is illustrated, and the tracking parameters are discussed one by one: the determination of the correlation window, the accuracy of specular delay guess, and the tracking-refresh period. Based on the results, the proposed European Space Agency Passive Reflectometry and Interferometry System In-Orbit Demonstrator tracking case study is examined. View full abstract»

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  • Contrast Enhancement Using Dominant Brightness Level Analysis and Adaptive Intensity Transformation for Remote Sensing Images

    Page(s): 62 - 66
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1129 KB) |  | HTML iconHTML  

    This letter presents a novel contrast enhancement approach based on dominant brightness level analysis and adaptive intensity transformation for remote sensing images. The proposed algorithm computes brightness-adaptive intensity transfer functions using the low-frequency luminance component in the wavelet domain and transforms intensity values according to the transfer function. More specifically, we first perform discrete wavelet transform (DWT) on the input images and then decompose the LL subband into low-, middle-, and high-intensity layers using the log-average luminance. Intensity transfer functions are adaptively estimated by using the knee transfer function and the gamma adjustment function based on the dominant brightness level of each layer. After the intensity transformation, the resulting enhanced image is obtained by using the inverse DWT. Although various histogram equalization approaches have been proposed in the literature, they tend to degrade the overall image quality by exhibiting saturation artifacts in both low- and high-intensity regions. The proposed algorithm overcomes this problem using the adaptive intensity transfer function. The experimental results show that the proposed algorithm enhances the overall contrast and visibility of local details better than existing techniques. The proposed method can effectively enhance any low-contrast images acquired by a satellite camera and is also suitable for other various imaging devices such as consumer digital cameras, photorealistic 3-D reconstruction systems, and computational cameras. View full abstract»

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  • Segmentation of Low-Cost Remote Sensing Images Combining Vegetation Indices and Mean Shift

    Page(s): 67 - 70
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (709 KB) |  | HTML iconHTML  

    The development of low-cost remote sensing systems is important in small agriculture business, particularly in developing countries, to allow feasible use of images to gather information. However, images obtained through such systems with uncalibrated cameras have often illumination variations, shadows, and other elements that can hinder the analysis by image processing techniques. This letter investigates the combination of vegetation indices (color index of vegetation extraction, visual vegetation index, and excess green) and the mean-shift algorithm, based on the local density estimation in the color space on images acquired by a low-cost system. The objective is to detect green coverage, gaps, and degraded areas. The results showed that combining local density estimation and vegetation indices improves the segmentation accuracy when compared with the competing methods. It deals well with images in different conditions and with regions of imbalanced sizes, confirming the practical application of the low-cost system. View full abstract»

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  • On Some Spectral Properties of TanDEM-X Interferograms Over Forested Areas

    Page(s): 71 - 75
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (530 KB) |  | HTML iconHTML  

    This letter reports about some observations over rainforests (in Brazil and Indonesia), where the spectra of TanDEM-X interferograms show distinct features, almost a signature, which is explained and modeled in terms of the scattering properties. Supported by comparisons with simulations, the observations exclude any homogeneous horizontally layered forest; instead, they are compatible with a model with point scatterers clustered in clouds. Such a model, with high extinction and large gaps that allow significant penetration, is able to explain to a good degree the observations. View full abstract»

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  • A New Approach to Change Detection in Multispectral Images by Means of ERGAS Index

    Page(s): 76 - 80
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (756 KB) |  | HTML iconHTML  

    In this letter, we propose a novel method for unsupervised change detection (CD) in multitemporal Erreur Relative Globale Adimensionnelle de Synthese (ERGAS) satellite images by using the relative dimensionless global error in synthesis index locally. In order to obtain the change image, the index is calculated around a pixel neighborhood (3 $times$ 3 window) processing simultaneously all the spectral bands available. With the objective of finding the binary change masks, six thresholding methods are selected. A comparison between the proposed method and the change vector analysis method is reported. The accuracy CD showed in the experimental results demonstrates the effectiveness of the proposed method. View full abstract»

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  • Hybrid Freeman/Eigenvalue Decomposition Method With Extended Volume Scattering Model

    Page(s): 81 - 85
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (594 KB) |  | HTML iconHTML  

    In this letter, an advanced version of the hybrid Freeman/eigenvalue decomposition technique for land parameter extraction is presented with an illustrative example of application. The motivation arises from decomposition problems in obtaining a meaningful volume scattering estimation, so that the technique can be used for both oriented objects and vegetation/forest areas. The idea is to improve the accuracy of the required parameter extraction. Two strategies are adopted to increase the applicability of a hybrid Freeman/eigenvalue decomposition technique: One is the unitary transformation of the coherency matrix; the other is to use an extended volume scattering model. The extension of the volume scattering model plays an essential role for the hybrid Freeman/eigenvalue decomposition technique. Since the volume scattering power is evaluated by assuming that the $HV$ component is caused by vegetation only in the existing technique, an extended volume scattering power approach is utilized. It is shown that vegetation areas and oriented objects such as urban building areas are well discriminated by the proposed technique as compared to the existing techniques. View full abstract»

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  • Building Detection in Very High Spatial Resolution Multispectral Images Using the Hit-or-Miss Transform

    Page(s): 86 - 90
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (344 KB) |  | HTML iconHTML  

    A method for building detection in very high spatial resolution multispectral images is presented. Buildings are detected using spectral and contextual information. First, potential building locations are enhanced on the basis of the spectral similarity between their roofs. To do this, the eigenvalue-based spectral similarity ratio is proposed. Next, the hit-or-miss transform (HMT) from mathematical morphology is used to assign pixels to buildings. To compute the HMT, fuzzy erosion and dilation are used. Additional processing based on size criteria is needed in some cases to separate buildings from roads. The method is tested on GeoEye and pan-sharpened Ikonos images. The preliminary results are promising. View full abstract»

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  • A New Model-Independent Method for Change Detection in Multitemporal SAR Images Based on Radon Transform and Jeffrey Divergence

    Page(s): 91 - 95
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (617 KB) |  | HTML iconHTML  

    This letter presents a new approach for change detection in multitemporal synthetic aperture radar images. Considering about the existence of speckle noise, the local statistics in a sliding window are compared instead of pixel-by-pixel comparison. Edgeworth series expansion is applied to estimate the probability density function (pdf), which is on the assumption that the pdf is not too far from normal distribution. To transcend such a limitation, in each analysis window, the image is projected onto two vectors in two independent dimensions; thus, the pdf of each projection is closer to a Gaussian density. In order to measure the distance between the two pairs of projections, the proposed algorithm uses a modified Kullback–Leibler (KL) divergence, called Jeffrey divergence, which turns out to be more numerically stable than KL divergence. Experiments on the real data show that the proposed detector outperforms all the others when a high detection rate is demanded. View full abstract»

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  • Classification of Very High Resolution SAR Images of Urban Areas Using Copulas and Texture in a Hierarchical Markov Random Field Model

    Page(s): 96 - 100
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (591 KB) |  | HTML iconHTML  

    This letter addresses the problem of classifying synthetic aperture radar (SAR) images of urban areas by using a supervised Bayesian classification method via a contextual hierarchical approach. We develop a bivariate copula-based statistical model that combines amplitude SAR data and textural information, which is then plugged into a hierarchical Markov random field model. The contribution of this letter is thus the development of a novel hierarchical classification approach that uses a quad-tree model based on wavelet decomposition and an innovative statistical model. The performance of the developed approach is illustrated on a high-resolution satellite SAR image of urban areas. View full abstract»

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  • Mitigating Range Ambiguities in High-PRF SAR With OFDM Waveform Diversity

    Page(s): 101 - 105
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (575 KB) |  | HTML iconHTML  

    Range-ambiguity suppression is a technical challenge for high-pulse-repetition-frequency (PRF) synthetic aperture radar (SAR). This letter proposes a practical approach to mitigate the range ambiguities in high-PRF SAR by using the orthogonal frequency-division multiplexing (OFDM) waveform diversity. The system scheme, waveform design, and range-ambiguity-to-signal-ratio performance are detailed. The approach eliminates the ambiguities, instead of just suppressing them like other techniques. The proposed OFDM chirp diverse waveform has a large time–bandwidth product. It is validated by computer-simulation results. Although OFDM radar has received much attention in recent years, there appears to be little work done in applying OFDM concepts to mitigate high-PRF radar range ambiguities, as is the subject of this letter. View full abstract»

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  • Analysis of Waveform Lidar Data Using Shape-Based Metrics

    Page(s): 106 - 110
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (230 KB) |  | HTML iconHTML  

    Models that use large-footprint waveform light detection and ranging (lidar) to estimate forest height, structure, and biomass have typically used either point data extracted from the waveforms or cumulative distributions of the waveform energy, disregarding potential information latent within the waveform shape. Shape-based metrics such as the centroid $C$ and the radius of gyration $RG$ can capture features missed by height-based metrics that are likely related to forest structure and biomass. Noise analyses demonstrated the relative insensitivity of $C$ and $RG$, supporting the hypothesis that these metrics could be used to identify similar shapes within noisy waveforms [such as the Laser Vegetation Imaging Sensor (LVIS) and Geoscience Laser Altimeter Sensor (GLAS)] or to discriminate among waveforms with different underlying shapes. These findings suggest that $C$ and $RG$ can be successfully used in future lidar studies of forest structure and that further research should be conducted to develop additional shape-based metrics, as well as to investigate the relationship between forest structure and lidar waveform shape. View full abstract»

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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.

 

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Meet Our Editors

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