IEEE Transactions on Geoscience and Remote Sensing (IEEE T GEOSCI REMOTE)

Publisher: Institute of Electrical and Electronics Engineers; IEEE Geoscience and Remote Sensing Society, Institute of Electrical and Electronics Engineers

Journal description

The theory, concepts, and techniques of science and engineering as applied to sensing the earth, oceans, atmosphere, and space; and the processing, interpretation, and dissemination of this information. This journal publishes technical papers disclosing new and significant research, reviews, tutorial papers, and correspondence articles discussing published articles or presenting timely information.

Current impact factor: 2.93

Impact Factor Rankings

2015 Impact Factor Available summer 2015
2013 / 2014 Impact Factor 2.933
2012 Impact Factor 3.467
2011 Impact Factor 2.895
2010 Impact Factor 2.47
2009 Impact Factor 2.234
2008 Impact Factor 3.157
2007 Impact Factor 2.344
2006 Impact Factor 1.752
2005 Impact Factor 1.627
2004 Impact Factor 1.467
2003 Impact Factor 1.867
2002 Impact Factor 1.603
2001 Impact Factor 1.605
2000 Impact Factor 1.485
1999 Impact Factor 1.732
1998 Impact Factor 1.251
1997 Impact Factor 1.419
1996 Impact Factor 1.218
1995 Impact Factor 1.233
1994 Impact Factor 1.356
1993 Impact Factor 0.741
1992 Impact Factor 0.905

Impact factor over time

Impact factor

Additional details

5-year impact 3.61
Cited half-life 8.10
Immediacy index 0.75
Eigenfactor 0.03
Article influence 0.93
Website IEEE Transactions on Geoscience and Remote Sensing website
Other titles IEEE transactions on geoscience and remote sensing, Institute of Electrical and Electronics Engineers transactions on geoscience and remote sensing, I.E.E.E. transactions on geoscience and remote sensing, Transactions on geoscience and remote sensing
ISSN 0196-2892
OCLC 5792014
Material type Periodical, Internet resource
Document type Journal / Magazine / Newspaper, Internet Resource

Publisher details

Institute of Electrical and Electronics Engineers

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  • Classification
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Publications in this journal

  • [Show abstract] [Hide abstract]
    ABSTRACT: A novel superpixel-based discriminative sparse model (SBDSM) for spectral–spatial classification of hyperspectral images (HSIs) is proposed. Here, a superpixel in a HSI is considered as a small spatial region whose size and shape can be adaptively adjusted for different spatial structures. In the proposed approach, the SBDSM first clusters the HSI into many superpixels using an efficient oversegmentation method. Then, pixels within each superpixel are jointly represented by a set of common atoms from a dictionary via a joint sparse regularization. The recovered sparse coefficients are utilized to determine the class label of the superpixel. In addition, instead of directly using a large number of sampled pixels as dictionary atoms, the SBDSM applies a discriminative K-SVD learning algorithm to simultaneously train a compact representation dictionary, as well as a discriminative classifier. Furthermore, by utilizing the class label information of training pixels and dictionary atoms, a class-labeled orthogonal matching pursuit is proposed to accelerate the K-SVD algorithm while still enforcing high discriminability on sparse coefficients when training the classifier. Experimental results on four real HSI datasets demonstrate the superiority of the proposed SBDSM algorithm over several well-known classification approaches in terms of both classification accuracies and computational speed.
    IEEE Transactions on Geoscience and Remote Sensing 08/2015; 53(8):1-16. DOI:10.1109/TGRS.2015.2392755
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    ABSTRACT: Based on the scattering mechanisms of multipath, a bistatic synthetic aperture radar (Bis-SAR) image intensity model for the composite ship–ocean (or other metallic targets with ocean) scene is developed in this paper through reasonably dealing with the path lengths of transmitter and receiver to the ship target. To get a Bis-SAR image intensity model for the composite ship–ocean scene, the Bis-SAR image intensity distribution for the ocean surface is analyzed, and the facet scattering model is used to give the individual returns from separate facets. In addition, the influence of velocity bunching (VB) modulation of long ocean waves on the distribution of radar cross section is also discussed. Finally, Bis-SAR imagery simulations of ocean wave and the composite ship–ocean scene are illustrated, and the results show that the proposed Bis-SAR image intensity model can be reasonable and the interaction effects between the ship and sea surface can make significant contributions to the Bis-SAR intensity.
    IEEE Transactions on Geoscience and Remote Sensing 08/2015; 53(8):1-9. DOI:10.1109/TGRS.2015.2393915
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    ABSTRACT: Subpixel translation estimation using phase correlation is a fundamental task for numerous applications in the remote sensing community. The major drawback of the existing subpixel phase correlation methods lies in their sensitivity to corruption, including aliasing and noise, as well as the poor performance in the case of practical remote sensing data. This paper presents a novel subpixel phase correlation method using singular value decomposition (SVD) and the unified random sample consensus (RANSAC) algorithm. In the proposed method, SVD theoretically converts the translation estimation problem to one dimensions for simplicity and efficiency, and the unified RANSAC algorithm acts as a robust estimator for the line fitting, in this case for the high accuracy, stability, and robustness. The proposed method integrates the advantages of Hoge's method and the RANSAC algorithm and avoids the corresponding shortfalls of the original phase correlation method based only on SVD. A pixel-to-pixel dense matching scheme on the basis of the proposed method is also developed for practical image registration. Experiments with both simulated and real data were carried out to test the proposed method. In the simulated case, the comparative results estimated from the generated synthetic image pairs indicate that the proposed method outperforms the other existing methods in the presence of both aliasing and noise, in both accuracy and robustness. Moreover, the pixel locking effect that commonly occurs in subpixel matching was also investigated. The degree of pixel locking effect was found to be significantly weakened by the proposed method, as compared with the original Hoge's method. In the real data case, experiments using different bands of ZY-3 multispectral sensor-corrected images demonstrate the promising performance and feasibility of the proposed method, which is able to identify seams of the image stitching between sub-charge-coupled device units.
    IEEE Transactions on Geoscience and Remote Sensing 08/2015; 53(8):4143-4156. DOI:10.1109/TGRS.2015.2391999
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    ABSTRACT: The capabilities of a new instrument for measuring the surface wave parameters are studied in detail. An acoustic wave gauge with a single antenna is designed to measure the backscatter intensity and the Doppler spectrum of the reflected acoustic signal and to retrieve the variance of the vertical orbital velocity component. The new instrument is numerically simulated, and field experiments using a prototype acoustic wave gauge are carried out. The comparison of the variances of the vertical orbital velocity component measured by acoustic and string wave gauges under field conditions, as well as the comparison of measurements and numerical simulation results, has confirmed the high accuracy of the retrieval algorithms. The advantage of the instrument is its capability of making measurements in any water basin without the use of a fixed platform.
    IEEE Transactions on Geoscience and Remote Sensing 08/2015; 53(8):1-8. DOI:10.1109/TGRS.2015.2396120
  • IEEE Transactions on Geoscience and Remote Sensing 08/2015; 53(8):1-7. DOI:10.1109/TGRS.2015.2393378
  • IEEE Transactions on Geoscience and Remote Sensing 08/2015; 53(8):10.
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    ABSTRACT: A novel method is presented for the characterization of dielectric grounds. The technique measures frequency shifts in a radar's crosstalk and links them to ground permittivity. The method is simple to implement and operates in real time. It may be implemented on the early-time signal of any ground-coupled radar and requires no knowledge of the system's antennas or feed structure. Accurate permittivity measurements are obtained, which may be then used to obtain depth estimates or as an input to inverse scattering and imaging techniques.
    IEEE Transactions on Geoscience and Remote Sensing 08/2015; 53(8):4157-4164. DOI:10.1109/TGRS.2015.2392110
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    ABSTRACT: Planar or cylindrical phased arrays are two candidate antennas for future polarimetric weather radar. These two candidate antennas have distinctly different attributes when used to make quantitative measurements of the polarimetric properties of precipitation. Of critical concern is meeting the required polarimetric performance for all directions of the electronically steered beam. The copolar and cross-polar radiation patterns and polarimetric parameter estimation performances of these two phased array antennas are studied and compared with that obtained using a dual-polarized parabolic reflector antenna. Results obtained from simulation show that the planar polarimetric phased array radar has unacceptable polarimetric parameter biases that require beam to beam correction, whereas biases obtained with the cylindrical polarimetric phased array radar are much lower and comparable to that obtained using the parabolic reflector antenna.
    IEEE Transactions on Geoscience and Remote Sensing 07/2015; 53(8):4313-4327. DOI:10.1109/TGRS.2015.2395714
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    ABSTRACT: In this paper, anomalous spatial gradients are investigated by an image processing method, known as singularity analysis, which is proposed to complement the current Advanced Scatterometer (ASCAT) quality control (QC) by using the singularity exponent (SE). The quality of ASCAT winds is known to be generally degraded, with increasing values of the inversion residual or maximum-likelihood estimator (MLE). In the current ASCAT Wind Data Processor (AWDP), an MLE-based QC is adopted to filter poor-quality winds, which has proven to be effective in screening artifacts in the ASCAT winds, associated with increased subcell wind variability and other phenomena such as confused sea state. However, some poorly verifying winds, which appear in areas with moist convection, are not screened by the operational QC. The extension of the QC procedure with SEs is investigated, based on a comprehensive analysis of quality-sensitive parameters, using the European Centre for Medium-range Weather Forecasts (ECMWF) model winds, the Tropical Rainfall Measuring Mission's (TRMM) Microwave Imager (TMI) rain data, and tropical buoy wind and precipitation data as reference, taking into account their spatial and temporal representation. The validation results show that the proposed method indeed effectively removes ASCAT winds in spatially variable conditions. It filters three times as many wind vectors as the operational QC, while preserving verification statistics with local buoys. We find that not the rain itself, but the extreme local wind variability associated with rain appears to generally decrease the consistency between ASCAT, buoy, and ECMWF winds.
    IEEE Transactions on Geoscience and Remote Sensing 07/2015; 53(8):1-13. DOI:10.1109/TGRS.2015.2392372
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    ABSTRACT: Land-use classification using remote sensing images covers a wide range of applications. With more detailed spatial and textural information provided in very high resolution (VHR) remote sensing images, a greater range of objects and spatial patterns can be observed than ever before. This offers us a new opportunity for advancing the performance of land-use classification. In this paper, we first introduce an effective midlevel visual elements-oriented land-use classification method based on “partlets,” which are a library of pretrained part detectors used for midlevel visual elements discovery. Taking advantage of midlevel visual elements rather than low-level image features, a partlets-based method represents images by computing their responses to a large number of part detectors. As the number of part detectors grows, a main obstacle to the broader application of this method is its computational cost. To address this problem, we next propose a novel framework to train coarse-to-fine shared intermediate representations, which are termed “sparselets,” from a large number of pretrained part detectors. This is achieved by building a single-hidden-layer autoencoder and a single-hidden-layer neural network with an $L0$-norm sparsity constraint, respectively. Comprehensive evaluations on a publicly available 21-class VHR land-use data set and comparisons with state-of-the-art approaches demonstrate the effectiveness and superiority of this paper.
    IEEE Transactions on Geoscience and Remote Sensing 07/2015; 53(8):1-12. DOI:10.1109/TGRS.2015.2393857
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    ABSTRACT: In this paper, we present a new model-based linear inversion approach for cross-borehole ground penetrating radar quantitative imaging. The approach is reliable and computationally effective as it consists of the cascade solution of two linear inverse problems. The first problem yields a qualitative image of the targets (i.e., their location and approximate shape) and the information needed to cast a set of virtual experiments wherein a linear scattering model that implicitly depends on unknown targets holds true. By relying on such a model, it is possible to achieve, via linear inversion, a quantitative estimate of the targets' electric permittivity and conductivity in a much broader range of cases as compared with traditional approximations, such as the Born approximation. The quantitative imaging capabilities of the proposed method are enhanced by means of an original strategy, in which the features of virtual experiments are exploited to counteract the data reduction caused by the aspect limitation of the measurement configuration. Results against simulated data are reported to show the capability to successfully image nonweak scatters.
    IEEE Transactions on Geoscience and Remote Sensing 07/2015; 53(8):4178-4185. DOI:10.1109/TGRS.2015.2392558
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    ABSTRACT: For long-wavelength space-based radars, such as the P-band radar on the recently selected European Space Agency BIOMASS mission, system distortions (crosstalk and channel imbalance), Faraday rotation, and system noise all combine to degrade the measurements. A first-order analysis of these effects on the measurements of the polarimetric scattering matrix is used to derive differentiable expressions for the errors in the polarimetric backscattering coefficients in the presence of Faraday rotation. Both the amplitudes and phases of the distortion terms are shown to be important in determining the errors and their maximum values. Exact simulations confirm the accuracy and predictions of the first-order analysis. Under an assumed power-law relation between $sigma_mathrm{hv} $ and the biomass, the system distortions and noise are converted into biomass estimation errors, and it is shown that the magnitude of the deviation of the channel imbalance from unity must be 4–5 dB less than the crosstalk, or it will dominate the error in the biomass. For uncalibrated data and midrange values of biomass, the crosstalk must be less than $-$24 dB if the maximum possible error in the biomass is to be within 20% of its true value. A less stringent condition applies if the amplitudes and phases of the distortion terms are considered random since errors near the maximum possible are very unlikely. For lower values of the biomass, the noise becomes increasingly important because the $sigma_mathrm{hv}$ signal-to-noise ratio is smaller.
    IEEE Transactions on Geoscience and Remote Sensing 07/2015; 53(8):4299-4312. DOI:10.1109/TGRS.2015.2395138
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    ABSTRACT: Satellite-retrieved aerosol optical depth (AOD) can potentially provide an effective way to complement the spatial coverage limitation of a ground particulate air-pollution monitoring network such as the U.S. Environment Protection Agency's regulatory monitoring network. One of the current state-of-the-art AOD retrieval methods is the National Aeronautics and Space Administration's Multiangle Imaging SpectroRadiometer (MISR) operational algorithm, which has a spatial resolution of 17.6 km $times$ 17.6 km. Although the MISR's aerosol products lead to exciting research opportunities to study particle composition at a regional scale, its spatial resolution is too coarse for analyzing urban areas, where the air pollution has stronger spatial variations and can severely impact public health and the environment. Accordingly, a novel AOD retrieval algorithm with a resolution of 4.4 km $times$ 4.4 km has been recently developed, which is based on hierarchical Bayesian modeling and the Monte Carlo Markov chain (MCMC) inference method. In this paper, we carry out detailed quantitative and qualitative evaluations of the new algorithm, which is called the HB-MCMC algorithm, using recent AErosol RObotic NETwork (AERONET) Distributed Regional Aerosol Gridded Observation Networks (DRAGON) campaign data obtained in the summer of 2011. These data, which were not available in a previous study, contain spatially dense ground measurements of the AOD and other aerosol particle characteristics from the Baltimore–Washington, DC region. Our results show that the HB-MCMC algorithm has 16.2% more AOD retrieval coverage and improves the root-mean-square error by 38.3% compared with the MISR operational algorithm. Our detailed analyses with various metrics show that the improvement of our scheme is coming from the novel mo- eling and inference method. Furthermore, the map overlay of the retrieval results qualitatively confirms the findings of the quantitative analyses.
    IEEE Transactions on Geoscience and Remote Sensing 07/2015; 53(8):4328-4339. DOI:10.1109/TGRS.2015.2395722
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    ABSTRACT: Robust classification methods are vital to the successful implementation of many material characterization techniques, particularly where large databases exist. In this paper, we demonstrate an extremely fast classification method for the identification of mineral mixtures in Raman spectroscopy using the large RRUFF database. However, this method is equally applicable to other techniques meeting the large database criteria, these including laser-induced breakdown, X-ray diffraction, and mass spectroscopy methods. Classification of these multivariate datasets can be challenging due in part to the various obscuring features inherently present within the underlying dataset and in part to the volume and variety of information known a priori. Some of the more specific challenges include the observation of mixtures with overlapping spectral features, the use of large databases (i.e., the number of predictors far outweighs the number of observations), the use of databases that contain groups of correlated spectra, and the ever present, clouding contaminants of noise, undesired background, and spectrometer artifacts. Although many existing classification algorithms attempt to address these problems individually, not many address them as a whole. Here, we apply a multistage approach, which leverages well-established constrained regression techniques, to overcome these challenges. Our modifications to conventional algorithm implementations are shown to increase speed and performance of the classification process. Unlike many other techniques, our method is able to rapidly classify mixtures while simultaneously preserving sparsity. It is easily implemented, has very few tuning parameters, does not require extensive parameter training, and does not require data dimensionality reduction prior to classification.
    IEEE Transactions on Geoscience and Remote Sensing 07/2015; 53(8):1-16. DOI:10.1109/TGRS.2015.2394377
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    ABSTRACT: Due to the intrinsic complexity of remote sensing images and the lack of prior knowledge, clustering for remote sensing images has always been one of the most challenging tasks in remote sensing image processing. Recently, clustering methods for remote sensing images have often been transformed into multiobjective optimization problems, making them more suitable for complex remote sensing image clustering. However, the performance of the multiobjective clustering methods is often influenced by their optimization capability. To resolve this problem, this paper proposes an adaptive multiobjective memetic fuzzy clustering algorithm (AFCMOMA) for remote sensing imagery. In AFCMOMA, a multiobjective memetic clustering framework is devised to optimize the two objective functions, i.e., $Jm$ and the Xie-Beni $(XB) $ index. One challenging task for memetic algorithms is how to balance the local and global search capabilities. In AFCMOMA, an adaptive strategy is used, which can adaptively achieve a balance between them, based on the statistical characteristic of the objective function values. In addition, in the multiobjective memetic framework, in order to acquire more individuals with high quality, a new population update strategy is devised, in which the updated population is composed of individuals generated in both the local and global searches. Finally, to evaluate the proposed AFCMOMA algorithm, experiments using three remote sensing images were conducted, which confirmed the effectiveness of the proposed algorithm.
    IEEE Transactions on Geoscience and Remote Sensing 07/2015; 53(8):4202-4217. DOI:10.1109/TGRS.2015.2393357
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    ABSTRACT: Radio waves traversing the Earth's ionosphere suffer from Faraday rotation with noticeable effects on measurements from lower frequency space-based radars, but these effects can be easily corrected given estimates of the Faraday rotation angle, i.e., $Omega$. Several methods to derive $Omega$ from polarimetric measurements are known, but they are affected by system distortions (crosstalk and channel imbalance) and noise. A first-order analysis for the most robust Faraday rotation estimator leads to a differentiable expression for the bias in the estimate of $Omega$ in terms of the amplitudes and phases of the distortion terms and the covariance properties of the target. The analysis applies equally to L-band and P-band. We derive conditions on the amplitudes and phases of the distortion terms that yield the maximum bias and a compact expression for its value for the important case where $Omega=0$. Exact simulations confirm the accuracy of the first-order analysis and verify its predictions. Conditions on the distortion amplitudes that yield a given maximum bias are derived numerically, and the maximum bias is shown to be insensitive to the amplitude of the channel imbalance terms. These results are important not just for correcting polarimetric data but also for assessing the accuracy of the estimates of the total electron content derived from Faraday rotation.
    IEEE Transactions on Geoscience and Remote Sensing 07/2015; 53(8):4284-4298. DOI:10.1109/TGRS.2015.2395076
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    ABSTRACT: Two-dimensional phase unwrapping is a key step in the phase extraction process, an image-processing stage that is common to many different systems. Many varied approaches have been proposed over the past several decades. However, with the growth of image scale, it poses new challenges in terms of computational and memory requirements to phase unwrapping that require a global approach to obtain good results. Owing to only a single process used in most previous algorithm implementations, it becomes more problematic to unwrapping when the required computing resources exceed the capability of one computer. Meanwhile, with the development and application of supercomputer techniques, high-performance computing is emerging as a promising platform for scientific applications. In this paper, a novel hybrid multiprocessing and multithreading algorithm is proposed in order to overcome the problem of unwrapping large data sets. In this algorithm, we improve on Goldstein's branch-cut algorithm using simulated annealing idea to further optimize the set of branch cuts in parallel. For large data sets, the tiling strategy based on the nature of parallel computing guarantees the globality of phase unwrapping and avoids large-scale errors introduced. Using real and simulated interferometric data, we demonstrate that our algorithms are highly competitive with other existing algorithms in speed and accuracy. We also demonstrate that the proposed algorithm can be efficiently parallelized and performed across nodes in a high-performance computing cluster.
    IEEE Transactions on Geoscience and Remote Sensing 07/2015; 53(7):3833-3846. DOI:10.1109/TGRS.2014.2385482