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
    ​ green

Publications in this journal

  • [Show abstract] [Hide abstract]
    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: 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: 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: This study proposes a microwave surface emission model for soil moisture retrieval using radiometer data based on today's most widely used physical model, i.e., advanced integral equation model (AIEM). Soil roughness and moisture effects are easily yet accurately decoupled in the proposed model. In the field case study, the total least squares method, instead of the least squares (LS) method, is applied for the first time in soil moisture retrieval to solve the error in variable linear equation set to further reduce the estimation error. Validated by the Soil Moisture Experiment 2003 campaign data in Oklahoma, the root mean square error (RMSE) and $R^{2} $ of volumetric soil moisture varies from 1.5% to 4.2% and 0.92 to 0.43 at L/C/X bands and 40/55° incidence angles. Compared with previous studies, the proposed model has several new features: 1) it is location independent since the model is derived through reproducing the behavior of the AIEM; 2) its high fidelity to AIEM significantly improves the accuracy, whereas its linearity makes it easy to invert; and 3) the soil moisture retrieval based on the proposed model requires no prior knowledge of soil roughness in the scenario of the demonstrated case study. The L-band/V-polarization radiometer data yield the best retrieval result with an RMSE of 1.5% and $R^{2} $ of 0.92, whereas increasing frequency increases the error because the sensitivity of emissivity to ground soil moisture decreases, and the valid roughness region, i.e., $kh_{RMS} < 3$, of the AIEM narrows.
    IEEE Transactions on Geoscience and Remote Sensing 07/2015; 53(7):4079-4090. DOI:10.1109/TGRS.2015.2390219
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    ABSTRACT: A physical approach to synthesize fractal surfaces for a reliable and controllable use within remote-sensing applications is presented in this paper. In particular, the physical criteria to determine the minimum number of tones of the Weierstrass–Mandelbrot (WM) function needed to adequately synthesize realizations of fractional Brownian motion (fBm) processes are analytically derived. The presented rationale relies on considering, in an appropriate range of scales, the WM function as a spectral sampling of the fBm process, and linking the number of sampling functions to the width of the scale range and to the sampling rate; hence, it is shown how to set the lower and the upper scales according to a given remote-sensing problem. In this paper, the condition for determining the WM wavenumber sampling rate is analytically derived. The method is also applied to the efficient simulation of the synthetic aperture radar signal scattered by natural surfaces.
    IEEE Transactions on Geoscience and Remote Sensing 07/2015; 53(7):3803-3814. DOI:10.1109/TGRS.2014.2384595
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    ABSTRACT: Many methods have been proposed to select sites for grid-scale soil moisture monitoring networks; however, calibration/validation activities also require information about where to place grid representative monitoring sites. In order to design a soil moisture network for this task in the Great Lakes Basin (522 000 km2), the dual-entropy multiobjective optimization algorithm was used to maximize the information content and minimize the redundancy of information in a potential soil moisture monitoring network. Soil moisture retrieved from the Soil Moisture and Ocean Salinity (SMOS) mission during the frost-free periods of 2010–2013 were filtered for data quality and then used in a multiobjective search to find Pareto optimum network designs based on the joint entropy and total correlation measures of information content and information redundancy, respectively. Differences in the information content of SMOS ascending and descending overpasses resulted in distinctly different network designs. Entropy from the SMOS ascending overpass was found to be spatially consistent, whereas descending overpass entropy had many peaks that coincided with areas of high subgrid heterogeneity. A combination of both ascending and descending overpasses produced network designs that incorporated aspects of information from each overpass. Initial networks were designed to include 15 monitoring sites, but the addition of network cost as an objective demonstrated that a network with similar information content could be achieved with fewer monitoring stations.
    IEEE Transactions on Geoscience and Remote Sensing 07/2015; 53(7):3950-3959. DOI:10.1109/TGRS.2014.2388451
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    ABSTRACT: This paper presents a generalized treatment of image formation for a linear-frequency-modulated continuous wave (LFM-CW) synthetic aperture radar (SAR) signal, which is a key technology in making very small SAR systems viable. The signal model is derived, which includes the continuous platform motion. The effect of this motion on the SAR signal is discussed, and an efficient compensation method is developed. Processing algorithms are developed including precise and approximate backprojection methods and a generalized frequency scaling algorithm that accounts for an arbitrary number of terms of a Taylor expansion approximation of the SAR signal in the Doppler frequency domain. Together, these algorithms allow for the processing of LFM-CW SAR data for a wide variety of system parameters, even in scenarios where traditional algorithms and signal approximations break down.
    IEEE Transactions on Geoscience and Remote Sensing 07/2015; 53(7):1-15. DOI:10.1109/TGRS.2014.2380154
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    ABSTRACT: Kernel methods with specifically designed kernel function are suitable for dealing with practical nonlinear problems. However, kernel methods have found limited applications to synthetic aperture radar (SAR) image change detection in that their performances are affected by the inherent multiplicative speckle noise of SAR images. It is known that the spatial-contextual information is helpful in suppressing the degrading effects of the noise. Therefore, a label-information composite kernel (LIC kernel) constructed on the basis of the spatial-contextual information is proposed in this paper for SAR image change detection. A typical spatial information, the output-space label-neighborhood information that is extracted using all labels in the neighborhood of each pixel, may enhance noise immunity, but with inaccurate edge locations simultaneously. Consequently, the anisotropic Gaussian kernel model is utilized for analyzing anisotropic textures of the bitemporal images, and then, a comparison scheme acting on the input-space textures of the bi-temporal images is proposed to supervise the extraction of the output-space label-neighborhood information in the construction of the LIC kernel. The constructed LIC kernel is of good preservation of edge locations of changed areas as well as strong noise immunity. The LIC kernel is updated iteratively with the newest change map outputted from the support vector machine, until the change map converges. Experiments on real SAR images demonstrate the effectiveness of the LIC kernel method and illustrate that it has both strong noise immunity and good preservation of edge locations of changed areas for SAR image change detection.
    IEEE Transactions on Geoscience and Remote Sensing 07/2015; 53(7):1-14. DOI:10.1109/TGRS.2015.2388495
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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
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    ABSTRACT: Object-based analysis of high spatial resolution remote sensing images addresses the matter of multiscale segmentation. However, existing segmentation evaluation methods mainly focus on single-scale segmentation. In this paper, we examine the issue of supervised multiscale segmentation evaluation and propose two discrepancy measures to determine the manner in which geographic objects are delineated by multiscale segmentations. A QuickBird scene in Hangzhou, China, is used to conduct the evaluation. The results reveal the effectiveness of the proposed measures, in terms of method comparison and parameter optimization, for multiscale segmentation of high spatial resolution images. Moreover, meaningful indications for selecting suitable multiple segmentation scales are presented. The proposed measures are applicable to performance evaluation and parameter optimization for multiscale segmentation algorithms.
    IEEE Transactions on Geoscience and Remote Sensing 07/2015; 53(7):1-13. DOI:10.1109/TGRS.2014.2381632
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    ABSTRACT: Synthetic aperture radar (SAR), in along-track interferometry mode, is extensively used in sensing oceanic surface. Detecting moving ships is becoming an increasingly important requirement in global monitoring of environment and maritime security. To realize this requirement, we first constructed a metric for detecting moving ships in SAR images by combining sea interferogram's magnitude and phase (SIMP). Second, the performance of the SIMP metric was evaluated through simulations and investigation of several key issues, such as azimuth ambiguity, minimum detectable velocity, and detection performance. The evaluation results establish the effectiveness of the proposed SIMP metric.
    IEEE Transactions on Geoscience and Remote Sensing 07/2015; 53(7):1-17. DOI:10.1109/TGRS.2014.2379352
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    ABSTRACT: We propose a novel class of schemes for the pansharpening of multispectral (MS) images using a multivariate empirical mode decomposition (MEMD) algorithm. MEMD is an extension of the empirical mode decomposition (EMD) algorithm, which enables the decomposition of multivariate data into its intrinsic oscillatory scales. The ability of MEMD to process multichannel data directly by performing data-driven, local, and multiscale analysis makes it a perfect match for pansharpening applications, a task for which standard univariate EMD is ill-equipped due to the nonuniqueness, mode-mixing, and mode-misalignment issues. We show that MEMD overcomes the limitations of standard EMD and yields improved spatial and spectral performance in the context of pansharpening of MS images. The potential of the proposed schemes is further demonstrated through comparative analysis against a number of standard pansharpening algorithms on both simulated Pleiades and real-world IKONOS data sets.
    IEEE Transactions on Geoscience and Remote Sensing 07/2015; 53(7):1-11. DOI:10.1109/TGRS.2015.2388497
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    ABSTRACT: Spectral unmixing is a popular technique for analyzing remotely sensed hyperspectral data sets with subpixel precision. Over the last few years, many algorithms have been developed for each of the main processing steps involved in spectral unmixing (SU) under the LMM assumption: 1) estimation of the number of endmembers; 2) identification of the spectral signatures of the endmembers; and 3) estimation of the abundance of endmembers in the scene. Although this general processing chain has proven to be effective for unmixing certain types of hyperspectral images, it also has some drawbacks. The first one comes from the fact that the output of each stage is the input of the following one, which favors the propagation of errors within the unmixing chain. A second problem is the huge variability of the results obtained when estimating the number of endmembers of a hyperspectral scene with different state-of-the-art algorithms, which influences the rest of the process. A third issue is the computational complexity of the whole process. To address the aforementioned issues, this paper develops a novel negative abundance-oriented SU algorithm that covers, for the first time in the literature, the main steps involved in traditional hyperspectral unmixing chains. The proposed algorithm can also be easily adapted to a scenario in which the number of endmembers is known in advance and two additional variations of the algorithm are provided to deal with high-noise scenarios and to significantly reduce its execution time, respectively. Our experimental results, conducted using both synthetic and real hyperspectral scenes, indicate that the presented method is highly competitive (in terms of both unmixing accuracy and computational performance) with regard to other SU techniques with similar requirements, while providing a fully self-contained unmixing chain without the need for any input parameters.
    IEEE Transactions on Geoscience and Remote Sensing 07/2015; 53(7):3772-3790. DOI:10.1109/TGRS.2014.2383440
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    ABSTRACT: This paper describes a method for detecting human respiratory motion with respiration-rate estimation using ultrawideband (UWB) synthetic aperture radar (SAR). In addition, positions of the breathing humans can be spatially resolved. The coherence of the SAR data is used to derive the generalized coherence factor (GCF), the generalized incoherence factor (GICF), and the filter-bank-based GCF (FBGCF) for the detection and estimation. The coherence is decreased by motion of the image object, and the GCF and GICF are used to detect the position of the moving object. Furthermore, since the spectral shift of SAR data varies with motion, the FBGCF can be used to determine the respiration rate. The efficacy of the proposed method was tested by constructing a UWB SAR system with a 1.5-GHz center frequency and a 1-GHz bandwidth. Through-wall SAR data of objects with various motions were acquired and analyzed. Moving objects were successively detected with a spectral resolution of 0.1 Hz, and using the GICF achieved a rejection ratio of 38 dB between stationary and moving objects. These results indicate that the FBGCF can be used for respiration-rate estimation.
    IEEE Transactions on Geoscience and Remote Sensing 07/2015; 53(7):1-15. DOI:10.1109/TGRS.2014.2382672