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: 3.51

Impact Factor Rankings

2016 Impact Factor Available summer 2017
2014 / 2015 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
Year

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

  • Pre-print
    • Author can archive a pre-print version
  • Post-print
    • Author can archive a post-print version
  • Conditions
    • Author's pre-print on Author's personal website, employers website or publicly accessible server
    • Author's post-print on Author's server or Institutional server
    • Author's pre-print must be removed upon publication of final version and replaced with either full citation to IEEE work with a Digital Object Identifier or link to article abstract in IEEE Xplore or replaced with Authors post-print
    • Author's pre-print must be accompanied with set-phrase, once submitted to IEEE for publication ("This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible")
    • Author's pre-print must be accompanied with set-phrase, when accepted by IEEE for publication ("(c) 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.")
    • IEEE must be informed as to the electronic address of the pre-print
    • If funding rules apply authors may post Author's post-print version in funder's designated repository
    • Author's Post-print - Publisher copyright and source must be acknowledged with citation (see above set statement)
    • Author's Post-print - Must link to publisher version with DOI
    • Publisher's version/PDF cannot be used
    • Publisher copyright and source must be acknowledged
  • Classification
    green

Publications in this journal

  • [Show abstract] [Hide abstract]
    ABSTRACT: In this paper, we propose an adaptive locality weighted multisource joint sparse representation classification (ALWMJ-SRC) model for the classification of multisource remote sensing data. Although the notion of multitask joint sparsity has been recently developed for data fusion and has shown to be effective for various applications, in this paper, we suggest that there are important limitations stemming from the assumptions in such a framework. We propose a formulation that is inspired by this approach yet addresses some of the key shortcomings (e.g., uniform weights and unstable estimation of coefficients), resulting in a more robust formulation for data fusion. Specifically, we impose an adaptive locality weight to constrain the sparse coefficients, which not only considers the locality information between the test sample and the atoms in the dictionary but also helps ensure that the coefficients are adaptively penalized, reducing estimation bias. The adaptive locality weight is calculated for each source, which ensures that complementary information is employed from different sources for fusion. The optimization problem is solved using an alternating-direction-methods-of-multipliers formulation. In addition, the proposed algorithm is extended to the kernel space. The efficacy of the proposed algorithm is validated via experiments for two fusion scenarios—spectral–spatial classification and hyperspectral-LiDAR sensor fusion. The experimental results demonstrate that ALWMJ-SRC consistently performs better than state-of-the-art classification approaches.
    No preview · Article · Jan 2016 · IEEE Transactions on Geoscience and Remote Sensing
  • [Show abstract] [Hide abstract]
    ABSTRACT: Robust estimates of precipitation in space and time are important for efficient natural resource management and for mitigating natural hazards. This is particularly true in regions with developing infrastructure and regions that are frequently exposed to extreme events. Gauge observations of rainfall are sparse but capture the precipitation process with high fidelity. Due to its high resolution and complete spatial coverage, satellite-derived rainfall data are an attractive alternative in data-sparse regions and are often used to support hydrometeorological early warning systems. Satellite-derived precipitation data, however, tend to underrepresent extreme precipitation events. Thus, it is often desirable to blend spatially extensive satellite-derived rainfall estimates with high-fidelity rain gauge observations to obtain more accurate precipitation estimates. In this research, we use two different methods, namely, ordinary kriging and k-nearest neighbor local polynomials, to blend rain gauge observations with the Climate Hazards Group Infrared Precipitation satellite-derived precipitation estimates in data-sparse Central America and Colombia. The utility of these methods in producing blended precipitation estimates at pentadal (five-day) and monthly time scales is demonstrated. We find that these blending methods significantly improve the satellite-derived estimates and are competitive in their ability to capture extreme precipitation.
    No preview · Article · Jan 2016 · IEEE Transactions on Geoscience and Remote Sensing
  • [Show abstract] [Hide abstract]
    ABSTRACT: Remote sensing images exhibit significant contrast and intensity regions and edges, which makes them highly suitable for using different texture features to properly represent and classify the objects that they contain. In this paper, we present a new technique based on multiple morphological component analysis (MMCA) that exploits multiple textural features for decomposition of remote sensing images. The proposed MMCA framework separates a given image into multiple pairs of morphological components (MCs) based on different textural features, with the ultimate goal of improving the signal-to-noise level and the data separability. A distinguishing feature of our proposed approach is the possibility to retrieve detailed image texture information, rather than using a single spatial characteristic of the texture. In this paper, four textural features: content, coarseness, contrast, and directionality (including horizontal and vertical), are considered for generating the MCs. In order to evaluate the obtained MCs, we conduct classification by using both remotely sensed hyperspectral and polarimetric synthetic aperture radar (SAR) scenes, showing the capacity of the proposed method to deal with different kinds of remotely sensed images. The obtained results indicate that the proposed MMCA framework can lead to very good classification performances in different analysis scenarios with limited training samples.
    No preview · Article · Jan 2016 · IEEE Transactions on Geoscience and Remote Sensing
  • [Show abstract] [Hide abstract]
    ABSTRACT: A high-resolution 3-D hydrodynamic model capable of simulating far wakes of ships has been implemented using computational fluid dynamics software. We feed the surface velocity field produced by the hydrodynamic model into a numerical radar imaging model to simulate synthetic aperture radar (SAR) signatures of the wake. Potential capabilities of this modeling method are demonstrated for an example of wind stress effects on the centerline (turbulent) ship wake. The numerical simulations show that an interaction of the wind-induced surface current with circulations in the ship wake results in a convergence zone on the upwind side of the centerline wake and a divergence zone on the downwind side. In the simulated radar image, the convergence zone appears to be bright because of enhanced surface roughness and radar backscattering. The divergence zone looks dark due to an attenuation of short gravity capillary waves and a corresponding reduction of the backscattered power. This combined hydrodynamic and radar imaging model predicts an asymmetry of the centerline wake with respect to the wind direction, which is consistent with observed ship wake signatures in high-resolution satellite SAR images. The approach developed in this work could be also useful for simulations of other natural and artificial fine-scale features on the sea surface (sharp frontal interfaces, freshwater plumes, etc.) and their interpretation in high-resolution SAR imagery.
    No preview · Article · Jan 2016 · IEEE Transactions on Geoscience and Remote Sensing
  • [Show abstract] [Hide abstract]
    ABSTRACT: Target detection in nonhomogeneous sea clutter environments is a complex and challenging task due to the capture effect from interfering outliers and the clutter edge effect from background intensity transitions. For synthetic aperture radar (SAR) measurements, those issues are commonly caused by multiple targets and meteorological and oceanographic phenomena, respectively. This paper proposes a segmentation-based constant false-alarm rate (CFAR) detection algorithm using truncated statistics (TS) for multilooked intensity (MLI) SAR imagery, which simultaneously addresses both issues. From our previous work, TS is a useful tool when the region of interest (ROI) is contaminated by multiple nonclutter pixels. Within each ROI confined by the reference window, the proposed scheme implements an automatic image segmentation algorithm, which performs a finite mixture model estimation with a modified expectation–maximization algorithm. Data truncation is applied here to exclude all possible statistically interfering classes, and sample modeling is based upon the truncated two-parameter gamma model. Next, CFAR detection is conducted pixel by pixel, utilizing the statistical information obtained from the segmentation process within the local reference window. The segmentation-based CFAR detection scheme is examined with real Radarsat-2 MLI SAR imagery. Compared with the conventional CFAR detection approaches, our proposal provides improved background clutter modeling and robust detection performance in nonhomogeneous clutter environments.
    No preview · Article · Jan 2016 · IEEE Transactions on Geoscience and Remote Sensing
  • [Show abstract] [Hide abstract]
    ABSTRACT: Parameter estimation of internal solitary waves (ISWs) is an important application of oceanic synthetic aperture radar (SAR) images. Several methods have been widely applied for estimating the parameters of ISWs. Most of these methods assume that the ISW signals are corrupted by additive receiver noise. However, the ISW signals in SAR images suffer from both additive (thermal) and multiplicative (speckle) noises. Therefore, the estimation accuracy of previously proposed methods could not reach the Cramér–Rao bound (CRB). This paper proposes an optimal parameter estimation method of ISWs and derives the CRB for parameter estimation of ISWs. The variances of the estimated ISW parameters are shown to reach the CRB. This optimum estimator is validated using ISW SAR signals taken from simulation data and European Remote Sensing 2 (ERS-2) images. The results show that the proposed method is more accurate and effective than other methods. In addition, it should be noted that this optimality could also refer to parameter estimation of other SAR extended targets with an analytical expression.
    No preview · Article · Jan 2016 · IEEE Transactions on Geoscience and Remote Sensing
  • [Show abstract] [Hide abstract]
    ABSTRACT: The trichromatic visualization of hundreds of bands in a hyperspectral image (HSI) has been an active research topic. The visualized image shall convey as much information as possible from the original data and facilitate easy image interpretation. However, most existing methods display HSIs in false color, which contradicts with user experience and expectation. In this paper, we propose a new framework for visualizing an HSI with natural color by the fusion of an HSI and a high-resolution color image via manifold alignment. Manifold alignment projects several data sets to a shared embedding space where the matching points between them are pairwise aligned. The embedding space bridges the gap between the high-dimensional spectral space of the HSI and the RGB space of the color image, making it possible to transfer natural color and spatial information in the color image to the HSI. In this way, a visualized image with natural color distribution and fine spatial details can be generated. Another advantage of the proposed method is its flexible data setting for various scenarios. As our approach only needs to search a limited number of matching pixel pairs that present the same object, the HSI and the color image can be captured from the same or semantically similar sites. Moreover, the learned projection function from the hyperspectral data space to the RGB space can be directly applied to other HSIs acquired by the same sensor to achieve a quick overview. Our method is also able to visualize user-specified bands as natural color images, which is very helpful for users to scan bands of interest.
    No preview · Article · Jan 2016 · IEEE Transactions on Geoscience and Remote Sensing
  • [Show abstract] [Hide abstract]
    ABSTRACT: This paper addresses the mitigation of wind turbine clutter (WTC) in weather radar data in order to increase the performance of existing weather radar systems and to improve weather analyses and forecasts. We propose a novel approach for this problem based on signal separation algorithms. We model the weather signal as group sparse in the time–frequency domain; in parallel, we model the WTC signal as having a sparse time derivative. In order to separate WTC and the desired weather returns, we formulate the signal separation problem as an optimization problem. The objective function to be minimized combines total variation regularization and time–frequency group sparsity. We also propose a three-window short-time Fourier transform for the time–frequency representation of the weather signal. To show the effectiveness of the proposed algorithm on weather radar systems, the method is applied to simulated and real data from the next-generation weather radar network. Significant improvements are observed in reflectivity, spectral width, and angular velocity estimates.
    No preview · Article · Jan 2016 · IEEE Transactions on Geoscience and Remote Sensing
  • [Show abstract] [Hide abstract]
    ABSTRACT: This paper develops and characterizes the algorithms used to generate the Level 1 (L1) science data products of the Cyclone Global Navigation Satellite System (CYGNSS) mission. The L1 calibration consists of two parts: the Level 1a (L1a) calibration converts the raw Level 0 delay–Doppler maps (DDMs) of processed counts into received power in units of watts. The L1a DDMs are then converted to Level 1b DDMs of bistatic radar cross section values by unwrapping the forward scattering model and generating two additional DDMs: one of unnormalized bistatic radar cross section values (in units of square meters) and a second of bin-by-bin effective scattering areas. The L1 data products are generated in such a way as to allow for flexible processing of variable areas of the DDM (which correspond to different regions on the surface). The application of the L1 data products to the generation of input observables for the CYGNSS Level 2 (L2) wind retrievals is also presented. This includes a demonstration of using only near-specular DDM bins to calculate a normalized bistatic radar cross section (unitless, i.e., m 2/m 2) over a subset of DDM pixels, or DDM area. Additionally, an extensive term-by-term error analysis has been performed using this example extent of the DDM to help quantify the sensitivity of the L1 calibration as a function of key internal instrument and external parameters in the near-specular region.
    No preview · Article · Jan 2016 · IEEE Transactions on Geoscience and Remote Sensing