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Geoscience and Remote Sensing Letters, IEEE

Issue 11 • Date Nov. 2014

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  • Front cover

    Publication Year: 2014 , Page(s): C1
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  • IEEE Geoscience and Remote Sensing Letters publication information

    Publication Year: 2014 , Page(s): C2
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  • Table of contents

    Publication Year: 2014 , Page(s): 1849 - 1850
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  • Microwave Imaging of a Slightly Varying 2-D Conducting Object Through Generalized Impedance Boundary Conditions

    Publication Year: 2014 , Page(s): 1851 - 1855
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (446 KB) |  | HTML iconHTML  

    We consider reconstructing the shape of a perfect electric conducting object and developed a novel imaging method based on generalized impedance boundary conditions (GIBCs). The method relies on selecting a fictitious surface encircling the unknown object in which the surface impedance is reconstructed from the scattered field measurements. Later, the shape reconstruction problem is cast into an equivalent problem where the distance variation between the fictitious impedance surface and the unknown target is determined from the reconstructed surface impedance. Since the performance of the method depends on the selection of the impedance surface, we present a selection criterion according to the validity conditions of GIBCs. The method is capable of reconstructing both convex and concave structures whose shape deviates, at most, a tenth of the wavelength from the impedance surface, as demonstrated with numerical results. View full abstract»

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  • A Semi-Automatic Method for Road Centerline Extraction From VHR Images

    Publication Year: 2014 , Page(s): 1856 - 1860
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1250 KB) |  | HTML iconHTML  

    This letter presents a semi-automatic approach to delineating road networks from very high resolution satellite images. The proposed method consists of three main steps. First, the geodesic method is used to extract the initial road segments that link the road seed points prescribed in advance by users. Next, a road probability map is produced based on these coarse road segments and a further direct thresholding operation separates the image into two classes of surfaces: the road and nonroad classes. Using the road class image, a kernel density estimation map is generated, upon which the geodesic method is used once again to link the foregoing road seed points. Experiments demonstrate that this proposed method can extract smooth correct road centerlines. View full abstract»

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  • Global Interferometric Coherence Maps From TanDEM-X Quicklook Data

    Publication Year: 2014 , Page(s): 1861 - 1865
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (572 KB) |  | HTML iconHTML  

    TanDEM-X is a spaceborne synthetic aperture radar (SAR) mission, whose goal is the generation of a global digital elevation model with unprecedented accuracy, by using SAR interferometry. One of the main parameters for asserting the quality of interferometric products is the coherence between the monostatic and bistatic images. The objective of this letter is to present the first global mosaics of the interferometric coherence generated from the TanDEM-X quicklook data set, achieving a resolution down to 25 × 25 ma. This is an improvement in terms of details by several orders of magnitude, with respect to the previously implemented techniques for monitoring the global TanDEM-X interferometric coherence. Critical performance areas are separately analyzed, focusing on the developed approach for optimizing the acquisition strategy, in order to achieve the final mission requirement. Moreover, TanDEM-X mosaics of the interferometric coherence show to be a promising starting point for land classification on a large scale. Finally, they represent a valuable input for the whole SAR community, allowing for the recognition of suitable test areas for further scientific purposes. View full abstract»

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  • High-Resolution SAR Image Generation by Subaperture Processing of FMCW Radar Signal

    Publication Year: 2014 , Page(s): 1866 - 1870
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (627 KB) |  | HTML iconHTML  

    In this letter, we present a subaperture-based processing scheme for synthetic aperture radar (SAR) imaging using frequency-modulated continuous-wave (FMCW) radar transmission. An efficient two-stage azimuth subaperture processing scheme is proposed for high-resolution SAR image generation. A new bulk range-cell-migration-correction technique is proposed. The algorithms are validated with point target simulations at the X-band and also using real data samples at the C-band. The results show that subaperture-based processing can be used to image extended ground scenes without mosaicing. View full abstract»

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  • Effective Contrast-Based Dehazing for Robust Image Matching

    Publication Year: 2014 , Page(s): 1871 - 1875
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (982 KB) |  | HTML iconHTML  

    In this letter we present a novel strategy to enhance images degraded by the atmospheric phenomenon of haze. Our single-based image technique does not require any geometrical information or user interaction enhancing such images by restoring the contrast of the degraded images. The degradation of the finest details and gradients is constrained to a minimum level. Using a simple formulation that is derived from the lightness predictor our contrast enhancement technique restores lost discontinuities only in regions that insufficiently represent original chromatic contrast of the scene. The parameters of our simple formulation are optimized to preserve the original color spatial distribution and the local contrast. We demonstrate that our dehazing technique is suitable for the challenging problem of image matching based on local feature points. Moreover, we are the first that present an image matching evaluation performed for hazy images. Extensive experiments demonstrates the utility of the novel technique. View full abstract»

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  • CEM: More Bands, Better Performance

    Publication Year: 2014 , Page(s): 1876 - 1880
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (639 KB) |  | HTML iconHTML  

    Target detection has recently drawn considerable interest in hyperspectral image processing. People tend to exclude corrupted or badly damaged bands before applying the target detection algorithm to the data for better detection results. In this letter, it is proved that adding any band independent of the original image, even a noisy band, would be always beneficial to the performance of constrained energy minimization in terms of output energy. Finally, several tests are conducted to further justify our viewpoint. View full abstract»

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  • Active Landmark Sampling for Manifold Learning Based Spectral Unmixing

    Publication Year: 2014 , Page(s): 1881 - 1885
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (706 KB) |  | HTML iconHTML  

    Nonlinear manifold learning based spectral unmixing provides an alternative to direct nonlinear unmixing methods for accommodating nonlinearities inherent in hyperspectral data. Although manifolds can effectively capture nonlinear features in the dimensionality reduction stage of unmixing, the computational overhead is excessive for large remotely sensed data sets. Manifold approximation using a set of distinguishing points is commonly utilized to mitigate the computational burden, but selection of these landmark points is important for adequately representing the topology of the manifold. This study proposes an active landmark sampling framework for manifold learning based spectral unmixing using a small initial landmark set and a computationally efficient backbone-based strategy for constructing the manifold. The active landmark sampling strategy selects the best additional landmarks to develop a more representative manifold and to increase unmixing accuracy. View full abstract»

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  • Singular Spectrum Analysis for Effective Feature Extraction in Hyperspectral Imaging

    Publication Year: 2014 , Page(s): 1886 - 1890
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (630 KB) |  | HTML iconHTML  

    As a very recent technique for time-series analysis, singular spectrum analysis (SSA) has been applied in many diverse areas, where an original 1-D signal can be decomposed into a sum of components, including varying trends, oscillations, and noise. Considering pixel-based spectral profiles as 1-D signals, in this letter, SSA has been applied in hyperspectral imaging for effective feature extraction. By removing noisy components in extracting the features, the discriminating ability of the features has been much improved. Experiments show that this SSA approach supersedes the empirical mode decomposition technique from which our work was originally inspired, where improved results in effective data classification using support vector machine are also reported. View full abstract»

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  • A Novel Processing Strategy for Staggered SAR

    Publication Year: 2014 , Page(s): 1891 - 1895
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (746 KB) |  | HTML iconHTML  

    Staggered synthetic aperture radar (SAR) is an innovative concept, where the pulse repetition interval (PRI) is continuously varied. This, in combination with digital beamforming (DBF) on receive, allows high-resolution imaging of a wide continuous swath without the need for a long antenna with multiple apertures. However, staggered-SAR systems require a mean pulse repetition frequency (PRF) much larger than the signal Doppler bandwidth and allow the use of transmitted pulses of limited length. This letter proposes a novel processing strategy for staggered SAR data, which allows a reduction of the mean PRF and the use of longer transmitted pulses. The performance obtained with the proposed novel strategy is evaluated and compared with a conventional SAR system operating with constant PRI. View full abstract»

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  • Comparison of Kinect and Terrestrial LiDAR Capturing Natural Karst Cave 3-D Objects

    Publication Year: 2014 , Page(s): 1896 - 1900
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (768 KB) |  | HTML iconHTML  

    Modeling natural phenomena from 3-D information enhances our understanding of the environment. Dense 3-D point clouds are increasingly used as highly detailed input datasets. In addition to the capturing techniques of point clouds with LiDAR, low-cost sensors have been released in the last few years providing access to new research fields and facilitating 3-D data acquisition for a broader range of applications. This letter presents an analysis of different speleothem features using 3-D point clouds acquired with the gaming device Microsoft Kinect. We compare the Kinect sensor with terrestrial LiDAR reference measurements using the KinFu pipeline for capturing complete 3-D objects (<; 4 m3). The results demonstrate the suitability of the Kinect to capture flowstone walls and to derive morphometric parameters of cave features. Although the chosen capturing strategy (KinFu) reveals a high correlation (R2 = 0.92) of stalagmite morphometry along the vertical object axis, a systematic overestimation (22% for radii and 44% for volume) is found. The comparison of flowstone wall datasets predominantly shows low differences (mean of 1 mm with 7 mm standard deviation) of the order of the Kinect depth precision. For both objects the major differences occur at strongly varying and curved surface structures (e.g., with fine concave parts). View full abstract»

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  • Efficient SOM-Based ATR Method for SAR Imagery With Azimuth Angular Variations

    Publication Year: 2014 , Page(s): 1901 - 1905
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (721 KB) |  | HTML iconHTML  

    The microwave imaging technique, especially for synthetic aperture radar (SAR), has significant advantages in providing high-resolution complex target images, even in darkness or adverse weather conditions. Nevertheless, it is still difficult for human operators to identify targets on SAR images because they are generated using radio signals with wavelengths at the order of cm. To deal with this, various approaches for efficient automatic target recognition (ATR), based on neural networks or support vector machines (SVM), have been developed. Previously we proposed a promising ATR method using a supervised self-organizing map (SOM), where a binarized SAR image is accurately classified by exploiting the unified distance matrix (U-matrix) metric. Although this method enhances ATR performance considerably, even with SAR images heavily contaminated by random noise, the calculation burden is enormous under expansions of scale and then cannot maintain the ATR performance, especially in cases with azimuth angle variations. In this letter, we propose a constrained learning scheme for generating the SOM and introduce the A-star algorithm to handle SOM scale expansion. Experimental investigations demonstrate the effectiveness of our proposed method. View full abstract»

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  • PUMA-SPA: A Phase Unwrapping Method Based on PUMA and Second-Order Polynomial Approximation

    Publication Year: 2014 , Page(s): 1906 - 1910
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (596 KB) |  | HTML iconHTML  

    This letter focuses on the phase unwrapping algorithm. A state-of-the-art phase unwrapping method called PUMA which is based on the max-flow/min-cut was proposed recently. The proposed method in this letter postprocesses the results of PUMA to improve the unwrapping results. A pointwise local second-order polynomial approximation method is considered to suppress the noise. We estimate the parameters of the polynomial by solving the overdetermined equations and get the solution with the Least Squares Error Fitting. The proposed algorithm synthesizes the unwrapping with the denoising method and is abbreviated as PUMA-SPA. In the denoising step, adaptive local window sizes are selected to compromise the fitting error and the suppression of noise. Experiments show that the proposed method can achieve better results than the method Congruence Operation and Least Squares Fitting (CO-LSF) proposed recently. View full abstract»

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  • A Model-Based Technique for the Automatic Detection of Earth Continental Ice Subsurface Targets in Radar Sounder Data

    Publication Year: 2014 , Page(s): 1911 - 1915
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (720 KB) |  | HTML iconHTML  

    The continuous melting of the ice at the Earth continental polar caps highlights the importance of an exhaustive study of the properties of the ice subsurface targets in order to provide a reliable analysis of their past and future evolution. Such study can be efficiently performed by automatically analyzing radargrams of the ice cross section acquired by radar sounder (RS) instruments. In this letter, we propose an automatic technique for a large-scale detection of the ice subsurface targets and the estimation of their properties (e.g., layered area thickness and bedrock scattering area) from radargrams acquired by RS operated at the Earth continental polar caps. This is done by using the parameters of the RS acquisition system combined with the output of an automatic image segmentation algorithm. The segmentation operation is applied to the radargrams after a preliminary processing phase aimed to emphasize the relevant subsurface targets. The segmentation criterion considers the radar signal backscattering properties and a model of the spatial distribution of the investigated targets that takes into account the effects of the wave propagation though the subsurface. Experimental results obtained on real radargrams acquired by an airborne RS in Antarctica confirm the effectiveness of the proposed technique. View full abstract»

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  • Adaptive Subspace Detection for Wideband Radar Using Sparsity in Sinc Basis

    Publication Year: 2014 , Page(s): 1916 - 1920
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (709 KB) |  | HTML iconHTML  

    The scenario that the moving range spread target (RST) contains the complicated motion is assumed in this letter, which means that its motion includes different nonconstant elements. Based on sparse representation, a new coherent integration method is proposed to improve the detection performance of the moving RST in Gaussian noise. Here, the sinc basis is introduced to sparsely represent the high-range-resolution profile (HRRP). Basis pursuit denoising (BPDN) recovers the HRRPs from their noisy measurements; hence, aligning the range bins can be implemented at low signal-to-noise ratios via the entropy minimization of adjacent coefficient vectors of the sparse HRRPs. Then, phase compensation is achieved by the recursive multiple-scatterer algorithm (RMSA) in order to acquire the coherent integration gain. Using the sinc basis, the adaptive subspace detector (ASD) is adopted to realize RST detection. Finally, the experimental results on raw data demonstrate the effectiveness of the proposed method. View full abstract»

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  • Estimation and Compensation of Phase Shifts in SAR Focusing of Spotlight Data Acquired With Discrete Antenna Steering

    Publication Year: 2014 , Page(s): 1921 - 1925
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (922 KB) |  | HTML iconHTML  

    Modern spaceborne synthetic aperture radar sensors are able to operate the spotlight mode to achieve very high resolution images at microwave frequencies. This mode is characterized by antenna steering to increase the illumination interval. The steering is carried out by beam switching on bursts during the data acquisition interval. In addition to an unavoidable spectrum modulation in azimuth, phase shifts can occur from burst to burst. In this letter, we describe the problem and a procedure able to estimate the phase shifts based on the image contrast maximization technique for subsequent compensation at the data focusing stage. Results on real data acquired by the COSMO-SkyMed sensor demonstrate the effectiveness of the proposed solution. View full abstract»

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  • An Improvement on the Complete Model-Based Decomposition of Polarimetric SAR Data

    Publication Year: 2014 , Page(s): 1926 - 1930
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (672 KB) |  | HTML iconHTML  

    An improvement on the complete three-component model-based decomposition (C3MD) of polarimetric synthetic aperture radar (SAR) data is proposed in this letter. When analyzing the scattering mechanism of the third component in the original C3MD, an orientation angle rotation (OAR) is used. However, the deorientation is found more suitable to be applied to analyze the scattering mechanism of the third component, because the deorientation minimizes the cross-polarization power of a coherency matrix so as to make the coherency matrix much closer to that of surface scattering or double-bounce scattering. With applying the deorientation instead of the OAR used in the original C3MD, an improved C3MD algorithm is proposed. Experiments with E-SAR data are presented to illustrate the effectiveness of the improvement. View full abstract»

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  • Compressive Sensing of Noisy Multispectral Images

    Publication Year: 2014 , Page(s): 1931 - 1935
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (506 KB) |  | HTML iconHTML  

    Compressive sensing of noisy multispectral images is considered in this letter. Multispectral images in remote sensing applications are multichannel and inherently noisy. An approach using Bregman split method for optimization in both spatial and transform domains is proposed. The performance of the proposed algorithm is evaluated by comparing with other approaches. It is shown that the proposed algorithm performs favorably compared with other approaches with noisy multispectral images in experiments. View full abstract»

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  • Prediction of Daily Global Solar Irradiation Using Temporal Gaussian Processes

    Publication Year: 2014 , Page(s): 1936 - 1940
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (285 KB) |  | HTML iconHTML  

    Solar irradiation prediction is an important problem in geosciences with direct applications in renewable energy. Recently, a high number of machine learning techniques have been introduced to tackle this problem, mostly based on neural networks and support vector machines. Gaussian process regression (GPR) is an alternative nonparametric method that provided excellent results in other biogeophysical parameter estimation. In this letter, we evaluate GPR for the estimation of solar irradiation. Noting the nonstationary temporal behavior of the signal, we develop a particular time-based composite covariance to account for the relevant seasonal signal variations. We use a unique meteorological data set acquired at a radiometric station that includes both measurements and radiosondes, as well as numerical weather prediction models. We show that the so-called temporal GPR outperforms ten state-of-the-art statistical regression algorithms (even when including time information) in terms of accuracy and bias, and it is more robust to the number of predictions used. View full abstract»

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  • Bilateral Filtering-Based Enhanced Pansharpening of Multispectral Satellite Images

    Publication Year: 2014 , Page(s): 1941 - 1945
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (650 KB) |  | HTML iconHTML  

    An efficient pansharpening method should inject the missing geometric information to the multispectral (MS) image while preserving its radiometric information. Widely used additive wavelet transform-based pansharpening methods extract the missing high-frequency information by decomposing the panchromatic (PAN) image and adding the detail layers to the low-resolution MS (LRM) image. However, this approach causes a redundant detail injection, leading to artifacts in the fusion result. In this letter, we propose to decompose the high-resolution-PAN image using an edge-preserving decomposition which will decrease the amount of redundant high-frequency injection. The missing high-frequency information of the LRM image is obtained by the decomposition of the PAN image using a multiscale bilateral filter. The spatial and range parameters of the bilateral filter are optimized so as to enhance spatial and spectral metrics. The fusion results are compared with the widely used additive wavelet luminance proportional (AWLP) and recently proposed improved AWLP fusion methods. The resulting images as well as evaluation metrics demonstrate that the proposed injection approach has better performance. View full abstract»

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  • On-the-Fly Extraction of Polyhedral Buildings From Airborne LiDAR Data

    Publication Year: 2014 , Page(s): 1946 - 1950
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (591 KB) |  | HTML iconHTML  

    This letter presents an on-the-fly method for extracting polyhedral buildings from airborne light detection and ranging (LiDAR) data. By using the gridding method, the planimetric position and elevation of laser footprints (normally treated as points) in the obtained scan line are mapped into a data sequence. Then, discrete stationary wavelet transform is applied to analyze the elevation variation in the sequence. Buildings in the scan line can be obtained from the detail wavelet coefficients of the sequence. Moreover, to improve precision of the extraction, the gradients of grid points in the geometric planes of building roofs along the direction of the scan line are calculated and remedied by using the corresponding gradients acquired from the adjacent scan lines. With the proposed on-the-fly method, polyhedral buildings in the scan area can be accurately extracted from laser points along the scan lines during the scanning process. The new method is validated by using a set of real airborne LiDAR data. View full abstract»

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  • Subsurface Soil Moisture Estimation by VI–LST Method

    Publication Year: 2014 , Page(s): 1951 - 1955
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (810 KB) |  | HTML iconHTML  

    In this letter, the relationship between temperature vegetation dryness index (TVDI) from the Moderate Resolution Imaging Spectroradiometer and subsurface soil moisture (SM) over crop and native grassland of the Argentine Pampas is analyzed. High correlation (R2 > 0.69) between TVDI and SM measurements was found at different soil depths. In addition, we found that the potential of this index to reflect subsurface soil wetness fluctuations depends on root system depth, root distribution in the soil, and physical soil characteristics. Results indicate that thermal and reflectance data combination could be used to monitor subsurface SM below vegetated areas. View full abstract»

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  • Measuring Arctic Sea Ice Motion in Real Time With Photogrammetry

    Publication Year: 2014 , Page(s): 1956 - 1960
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (663 KB) |  | HTML iconHTML  

    The U.S. Naval Research Laboratory (NRL) has been collecting sea ice data in the Arctic, off the northern coast of Alaska, with an airborne system employing a radar altimeter, lidar, and a photogrammetric camera in an effort to obtain wide swaths of measurements coincident with CryoSat-2 track footprints. Because the satellite tracks traverse regions of moving pack ice, and the aircraft speed and measurement footprint are smaller than that of the satellite, it is necessary to know the local ice motion in order to plan and fly a full-coverage survey. With the advent of functional and real-time orthographic photogrammetric systems, we have developed a Real-Time Ice Motion Estimation (RTIME) system that permits the rapid determination of sea ice motion. RTIME enables tracking of specific patches of ice, allowing direct comparison of airborne data to satellite data on a point-by-point basis. This system should be of utility to other Arctic airborne science programs. 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