IEEE Transactions on Geoscience and Remote Sensing Journal Impact Factor & Information

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: 3.51

Impact Factor Rankings

2015 Impact Factor Available summer 2016
2014 Impact Factor 3.514
2013 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 4.11
Cited half-life 8.70
Immediacy index 0.86
Eigenfactor 0.04
Article influence 1.11
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

Publications in this journal

  • [Show abstract] [Hide abstract]
    ABSTRACT: A 1.5- $mumbox{m}$ all-fiber coherent Doppler lidar system for wind sensing was developed and characterized. The system design and implementation is presented. The system employs a 20-kHz pulse repetition rate transmitter and samples return signals at 400 MHz. A field-programmable gate array is programmed to generate and accumulate real-time periodograms representing average power spectra of the Doppler shifted echo from a series of more than 60 range gates, where each range gate is 48 m. The accumulated periodograms are streamed to a host computer for subsequent processing to yield the line-of-sight wind velocity. Wind velocity estimates with a precision value of 0.08 m/s can be made under nominal aerosol loading and nominal atmospheric turbulence conditions for ranges up to 3 km. Wind velocities and aerosol profiles are obtained while scanning the pointing direction of the lidar, thus producing both horizontal and vertical representations of the wind vectors.
    IEEE Transactions on Geoscience and Remote Sensing 12/2015; 53(12):6495-6506. DOI:10.1109/TGRS.2015.2442955
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    ABSTRACT: Satellite-based snow-cover monitoring is performed using optical, synthetic aperture radar (SAR), and passivemicrowave sensors. Effects of forest canopy on the observed signal need to be considered with all of these sensor types. Various models describing the interaction of electromagnetic radiation with forest canopy have been developed, but many of these are overly complex with high computational and ancillary data requirements. However, for retrieval purposes, simple models are preferred. This work aims at increasing the understanding of the effect of forest canopy on remote sensing observations of snow-covered terrain for both microwave and optical regimes and at quantifying the capability of simple zeroth-order models in simulating these effects. To achieve these goals, a spatial analysis of optical, SAR, and passive-microwave remote sensing data in the northern boreal forest region was performed. Model parameters for vegetation transmissivity as well as the properties of the underlying surface were optimized by utilizing lidar-ranging- and Landsat-based simplified proxy parameters describing forest canopy closure and stem volume. The results demonstrated that despite using these relatively simple proxies, a zeroth-order model can accurately estimate the extinction of electromagnetic signals in a forest, particularly for passive microwave and optical data. The SAR model successfully estimated the median of the observations, but larger scatter of the observations was reflected by a higher root mean square error and lower correlation between models and observations. Due to both good estimation accuracy and simplicity, the presented models can be considered to be applicable in existing snow retrieval algorithms.
    IEEE Transactions on Geoscience and Remote Sensing 12/2015; 53(12):6593-6607. DOI:10.1109/TGRS.2015.2444422
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    ABSTRACT: Layover affects the quality of urban interferometric synthetic aperture radar (InSAR) digital elevation models. Moreover, it is generally difficult to interpret because of the superposition of several contributions in a single SAR pixel. In this paper, a novel technique for the extraction of building layovers is first presented. It makes use of the geocoding stage embedded in the InSAR processor. It is shown that building layovers create a regular pattern in the mapping counter, a map describing the number of occurrences of a SAR pixel in the elevation model. Its exploitation yields a generation of a layover map without the use of external supports. The integration in the processor with a limited additional computational load and the capability to isolate layover signatures are additional benefits. Layover patches are then individually analyzed toward a better understanding of the complex urban signal return. A spectral estimation framework is employed to assess the slopes superimposed in the patches. Fringe-frequency estimation is involved. A set of simulations made for a nonparametric (fast Fourier transform) and a parametric (multiple signal classification) technique is performed prior to testing on real data. It is demonstrated that in X-band, for a single interferogram, just one layover contributor, when it dominates over the others, can be extracted with a sufficient accuracy. The algorithms are tested on a TanDEM-X spotlight acquisition over Berlin (Germany).
    IEEE Transactions on Geoscience and Remote Sensing 12/2015; 53(12):6457-6468. DOI:10.1109/TGRS.2015.2440913
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    ABSTRACT: A novel multiple-instance hidden Markov model (MI-HMM) is introduced for classification of time-series data, and its training is developed using stochastic expectation maximization. The MI-HMM provides a single statistical form to learn the parameters of an HMM in a multiple-instance learning framework without introducing any additional parameters. The efficacy of the model is shown both on synthetic data and on a real landmine data set. Experiments on both the synthetic data and the landmine data set show that an MI-HMM can 1) achieve statistically significant performance gains when compared with the best existing HMM for the landmine detection problem, 2) eliminate the ad hoc approaches in training set selection, and 3) introduce a principled way to work with ambiguous time-series data.
    IEEE Transactions on Geoscience and Remote Sensing 12/2015; 53(12):6766-6775. DOI:10.1109/TGRS.2015.2447576
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    ABSTRACT: Linear spectral unmixing is nowadays an essential tool to analyze remotely sensed hyperspectral images. Although many different contributions have been uncovered during the last two decades, the majority of them are based on dividing the whole process of linearly unmixing a given hyperspectral image into three sequential steps: 1) estimation of the number of endmembers that are present in the hyperspectral image under consideration; 2) extraction of these endmembers from the hyperspectral data set; and 3) calculation of the abundances associated with the endmembers induced in the previous step per each mixed pixel of the image. Although this de facto processing chain has proven to be accurate enough for unmixing most of the images collected by hyperspectral remote sensors, it is also true that it is not exempt of drawbacks, such as the fact that all the possible combinations of algorithms in order to fully unmix a hyperspectral image according to the aforementioned processing chain demand a formidable computational effort, which tends to be higher the better the performance of the designed unmixing chain is. This troublesome issue unfortunately prevents the use of hyperspectral imaging technology in applications under real-time constraints, in which hyperspectral images have to be analyzed in a short period of time. Hence, there is a clear need to face the challenge of fully exploiting the unquestionable benefits of the hyperspectral imaging technology for these applications, but concurrently overcoming the limitations imposed by the computationally complex nature of the processes involved. For this purpose, this paper introduces a novel algorithm named fast algorithm for linearly unmixing hyperspectral images (FUN), which is capable of fully unmixing a hyperspectral image with at least the same accuracy than state-of-the-art approaches while demanding a much lower computational effort, independent of the characteristics of the image under analysis- The FUN algorithm is based on the concept of orthogonal projections and allows performing the estimation of the number of endmembers and their extraction simultaneously, using the modified Gram–Schmidt method. The operations performed by the FUN algorithm are simple and can be easily parallelized. Moreover, this algorithm is able to calculate the abundances using very similar operations, also based on orthogonal projections, which makes it easier to achieve a hardware implementation to perform the entire unmixing process. The benefits of our proposal are demonstrated with a diverse set of artificially generated hyperspectral images and with the well-known AVIRIS Cuprite image, for which the proposed FUN algorithm is able to reduce in a factor of more than 31 times the time required for processing it, while providing a better unmixing performance than traditional methods.
    IEEE Transactions on Geoscience and Remote Sensing 12/2015; 53(12):1-14. DOI:10.1109/TGRS.2015.2447573
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    ABSTRACT: During the period of processing and analysis of on-orbit radiometric calibration data received from a space-based infrared camera launched recently, three practical issues were extracted and resolved at the data level, specifically how to exclude the invalid calibration data; how to determine the appropriate on-orbit decontamination time in the presence of increasing contaminants inside the camera system; and how to calibrate images without suitable calibration coefficients. Three major types of invalid data were summarized according to their appearances and possible causes after analyzing data from many on-orbit calibration tests, and the targeted filtering strategies were proposed with proven excellent performance in practice. A two-term exponential model was established to characterize the observed camera degradation by dividing the corresponding digital number into the blackbody and non-blackbody terms. Based on the model, degradation trends of the camera response, radiance resolution, and signal-to-noise ratio were estimated, respectively, to help determine the contamination tolerance from different aspects. Attempts were made to predict calibration coefficients by the pixelwise degradation models, and then they were applied to image calibration. Results show that the predicted coefficients can effectively compensate the calibration errors due to degradation and can be treated as an alternative if time-matched coefficients are unavailable.
    IEEE Transactions on Geoscience and Remote Sensing 12/2015; 53(12):1-13. DOI:10.1109/TGRS.2015.2438291
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    ABSTRACT: The challenge in the imaging of high-squint-mode synthetic aperture radar (SAR) mounted on maneuvering platforms is the azimuth dependence of both the range migration and the azimuth focusing parameters (the azimuth frequency-modulation rate and higher order coefficients), which are caused by range walk correction and acceleration. In order to accommodate the dependence, a modified subaperture imaging algorithm is proposed. Based on the fact that the azimuth times corresponding to the same Doppler frequency are different for targets in the same range gate, after making blocks in the azimuth frequency domain, the azimuth-dependent range cell migration correction is performed in the azimuth time domain for each block. Considering that the regions of support in the azimuth frequency domain are different for targets in the same range gate, the equalization of the azimuth focusing parameters is achieved by a new azimuth nonlinear chirp scaling method in the azimuth frequency domain. In order to verify the effectiveness of the proposed algorithm, the simulation of a point target array is presented. Furthermore, the real SAR data with a squint angle of 70° are processed, and high-quality images with a resolution of 1 m are provided.
    IEEE Transactions on Geoscience and Remote Sensing 12/2015; 53(12):6718-6734. DOI:10.1109/TGRS.2015.2447393
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    ABSTRACT: Estimation of forest height from combined polarimetric and interferometric synthetic aperture radar (Pol-InSAR) measurements has been the focus of radar remote sensing studies in the past decade. The simplicity of the random-volume-over-ground (RVoG) model makes it one of the most widely used candidates for estimating canopy height. However, the polarization-independent extinction coefficient assumption in the RVoG model fails in some certain types of the canopies, as suggested by the oriented-volume-over-ground (OVoG) model. The sensitivity of coherence magnitude and phase to different parameters of the canopy is expressed in a closed-form formulation in this paper for the first time. In order to simplify our formulation, the forest is represented by a layer of discrete randomly distributed dielectric scatterers over ground, with azimuthal symmetry. The sensitivity analysis of this work quantifies the contribution of differential extinction due to polarization change in interferometric coherence. Therefore, we can quantitatively evaluate whether the RVoG model is accurate enough to be used for a specific kind of canopy or the OVoG model is needed for better estimation. A simple layer of leaves over ground is used to simulate the sensitivity of Pol-InSAR measurements to different parameters.
    IEEE Transactions on Geoscience and Remote Sensing 12/2015; 53(12):6561-6572. DOI:10.1109/TGRS.2015.2444351

  • IEEE Transactions on Geoscience and Remote Sensing 12/2015; DOI:10.1109/TGRS.2015.2496348
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    ABSTRACT: In the traditional model of monostatic synthetic aperture radar (SAR), the flying platform should have a uniform linear trajectory. In practice, the flight path of the SAR platform, which is highly nonlinear or curvy caused by 3-D velocity and acceleration, cannot be used in the traditional model. In this paper, a geometrical model of the monostatic SAR with general configurations is graphically illustrated by vector notation. Moreover, the gradient method is utilized to point out the effects of the motion parameters, namely, velocity vector and acceleration vector, on total bandwidth and image resolution. Based on the accurate model, a general frequency-domain algorithm, with the incorporations of pre- and postprocessing, is proposed to focus the raw data. Applicability is studied through theoretical analysis and numerical experiments. All these general analyses can be applied to different realizations of monostatic SAR mode, including spotlight SAR, sliding spotlight SAR, and Terrain Observation by Progressive Scans (TOPS) SAR.
    IEEE Transactions on Geoscience and Remote Sensing 12/2015; 53(12):6529-6546. DOI:10.1109/TGRS.2015.2443835