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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing — Template for authors

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Guideline source

Categories	Rank	Trend in last 3 yrs
Computers in Earth Sciences	#7 of 52	down by 2 ranks
Atmospheric Science	#18 of 124	down by 2 ranks

Journal quality:

High

Last 4 years overview: 1836 Published Papers | 13185 Citations

Indexed in: Scopus

Last updated: 12/06/2020

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Insights

General info

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SJR: 0.518

SNIP: 0.73

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SNIP: 2.93

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CiteRatio: 4.1

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SNIP: 1.154

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Journal Performance & Insights

Impact Factor

CiteRatio

Determines the importance of a journal by taking a measure of frequency with which the average article in a journal has been cited in a particular year.

A measure of average citations received per peer-reviewed paper published in the journal.

3.827

13% from 2018

Impact factor for IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing from 2016 - 2019
Year	Value
2019	3.827
2018	3.392
2017	2.777
2016	2.913

Graph view

7.2

1% from 2019

CiteRatio for IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing from 2016 - 2020
Year	Value
2020	7.2
2019	7.3
2018	6.7
2017	6.2
2016	5.1

Graph view

Insights

Impact factor of this journal has increased by 13% in last year.
This journal’s impact factor is in the top 10 percentile category.

Insights

CiteRatio of this journal has decreased by 1% in last years.
This journal’s CiteRatio is in the top 10 percentile category.

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

1.246

16% from 2019

SJR for IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing from 2016 - 2020
Year	Value
2020	1.246
2019	1.48
2018	1.508
2017	1.547
2016	1.595

Graph view

1.579

7% from 2019

SNIP for IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing from 2016 - 2020
Year	Value
2020	1.579
2019	1.699
2018	1.882
2017	1.648
2016	1.937

Graph view

Insights

SJR of this journal has decreased by 16% in last years.
This journal’s SJR is in the top 10 percentile category.

Insights

SNIP of this journal has decreased by 7% in last years.
This journal’s SNIP is in the top 10 percentile category.

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

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IEEE

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

The “IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing” (JSTARS) is a new quarterly publication sponsored by the IEEE Geoscience and Remote Sensing Society (GRSS) and co-sponsored by the IEEE Committee on Earth Observations (ICEO). The new Journal reflects the growing interest in application themes in the annual IEEE conferences as well as the increasing involvement of GRSS in the ICEO. A new journal was envisaged as a communication and outreach medium for these applications themes. The ICEO is a committee of the Technical Activities Board of IEEE (TAB). The ICEO includes the interests of several IEEE committees and councils, and members have taken leadership roles in activities of the Global Earth Observations System of Systems (GEOSS). A new journal has been part of the development plan of ICEO from its inception. A partnership between GRSS and ICEO on developing the new journal has been an obvious opportunity and, from the beginning, both groups have collaborated on the planning for the new cosponsored journal. It will be a venue for peer reviewed papers on a variety of application themes in earth observations and remote sensing of relevance to the membership of both groups. Read Less

Computers in Earth Sciences

Atmospheric Science

Earth and Planetary Sciences

Last updated on	12 Jun 2020
ISSN	1939-1404
Impact Factor	High - 2.015
Open Access	No
Sherpa RoMEO Archiving Policy	Green
Plagiarism Check	Available via Turnitin
Endnote Style	Download Available
Bibliography Name	IEEEtran
Citation Type	Numbered [25]
Bibliography Example	C. W. J. Beenakker, “Specular andreev reflection in graphene,” Phys. Rev. Lett., vol. 97, no. 6, p.

Top papers written in this journal

Open access • Journal Article • DOI: 10.1109/JSTARS.2012.2194696 •

Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches

Jose M. Bioucas-Dias¹, Antonio Plaza², •

15 May 2012 - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Abstract:

Imaging spectrometers measure electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than multispectral cameras. Imaging spectrometers are therefore often referred to as hyperspectral cameras (HSCs). Higher spectral resolution enables ma... Imaging spectrometers measure electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than multispectral cameras. Imaging spectrometers are therefore often referred to as hyperspectral cameras (HSCs). Higher spectral resolution enables material identification via spectroscopic analysis, which facilitates countless applications that require identifying materials in scenarios unsuitable for classical spectroscopic analysis. Due to low spatial resolution of HSCs, microscopic material mixing, and multiple scattering, spectra measured by HSCs are mixtures of spectra of materials in a scene. Thus, accurate estimation requires unmixing. Pixels are assumed to be mixtures of a few materials, called endmembers. Unmixing involves estimating all or some of: the number of endmembers, their spectral signatures, and their abundances at each pixel. Unmixing is a challenging, ill-posed inverse problem because of model inaccuracies, observation noise, environmental conditions, endmember variability, and data set size. Researchers have devised and investigated many models searching for robust, stable, tractable, and accurate unmixing algorithms. This paper presents an overview of unmixing methods from the time of Keshava and Mustard's unmixing tutorial to the present. Mixing models are first discussed. Signal-subspace, geometrical, statistical, sparsity-based, and spatial-contextual unmixing algorithms are described. Mathematical problems and potential solutions are described. Algorithm characteristics are illustrated experimentally. read more

Topics:

Hyperspectral imaging (57%)57% related to the paper, Endmember (53%)53% related to the paper, Multispectral image (52%)52% related to the paper

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2,373 Citations

Journal Article • DOI: 10.1109/JSTARS.2014.2329330 •

Deep Learning-Based Classification of Hyperspectral Data

Yushi Chen¹, Zhouhan Lin¹, •

26 Jun 2014 - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Abstract:

Classification is one of the most popular topics in hyperspectral remote sensing. In the last two decades, a huge number of methods were proposed to deal with the hyperspectral data classification problem. However, most of them do not hierarchically extract deep features. In this paper, the concept of deep learning is introdu... Classification is one of the most popular topics in hyperspectral remote sensing. In the last two decades, a huge number of methods were proposed to deal with the hyperspectral data classification problem. However, most of them do not hierarchically extract deep features. In this paper, the concept of deep learning is introduced into hyperspectral data classification for the first time. First, we verify the eligibility of stacked autoencoders by following classical spectral information-based classification. Second, a new way of classifying with spatial-dominated information is proposed. We then propose a novel deep learning framework to merge the two features, from which we can get the highest classification accuracy. The framework is a hybrid of principle component analysis (PCA), deep learning architecture, and logistic regression. Specifically, as a deep learning architecture, stacked autoencoders are aimed to get useful high-level features. Experimental results with widely-used hyperspectral data indicate that classifiers built in this deep learning-based framework provide competitive performance. In addition, the proposed joint spectral-spatial deep neural network opens a new window for future research, showcasing the deep learning-based methods' huge potential for accurate hyperspectral data classification. read more

Topics:

Deep belief network (66%)66% related to the paper, Deep learning (56%)56% related to the paper, Hyperspectral imaging (54%)54% related to the paper,

2,071 Citations

Journal Article • DOI: 10.1109/JSTARS.2015.2388577 •

Spectral–Spatial Classification of Hyperspectral Data Based on Deep Belief Network

Yushi Chen¹, Xing Zhao¹, •

23 Jan 2015 - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Abstract:

Hyperspectral data classification is a hot topic in remote sensing community. In recent years, significant effort has been focused on this issue. However, most of the methods extract the features of original data in a shallow manner. In this paper, we introduce a deep learning approach into hyperspectral image classification.... Hyperspectral data classification is a hot topic in remote sensing community. In recent years, significant effort has been focused on this issue. However, most of the methods extract the features of original data in a shallow manner. In this paper, we introduce a deep learning approach into hyperspectral image classification. A new feature extraction (FE) and image classification framework are proposed for hyperspectral data analysis based on deep belief network (DBN). First, we verify the eligibility of restricted Boltzmann machine (RBM) and DBN by the following spectral information-based classification. Then, we propose a novel deep architecture, which combines the spectral–spatial FE and classification together to get high classification accuracy. The framework is a hybrid of principal component analysis (PCA), hierarchical learning-based FE, and logistic regression (LR). Experimental results with hyperspectral data indicate that the classifier provide competitive solution with the state-of-the-art methods. In addition, this paper reveals that deep learning system has huge potential for hyperspectral data classification. read more

Topics:

Deep belief network (62%)62% related to the paper, Contextual image classification (58%)58% related to the paper, Hyperspectral imaging (58%)58% related to the paper,

1,028 Citations

Journal Article • DOI: 10.1109/JSTARS.2009.2020300 •

Irradiance Forecasting for the Power Prediction of Grid-Connected Photovoltaic Systems

Elke Lorenz¹, Johannes Hurka¹, •

28 Apr 2009 - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Abstract:

The contribution of power production by photovoltaic (PV) systems to the electricity supply is constantly increasing. An efficient use of the fluctuating solar power production will highly benefit from forecast information on the expected power production. This forecast information is necessary for the management of the elect... The contribution of power production by photovoltaic (PV) systems to the electricity supply is constantly increasing. An efficient use of the fluctuating solar power production will highly benefit from forecast information on the expected power production. This forecast information is necessary for the management of the electricity grids and for solar energy trading. This paper presents an approach to predict regional PV power output based on forecasts up to three days ahead provided by the European Centre for Medium-Range Weather Forecasts (ECMWF). Focus of the paper is the description and evaluation of the approach of irradiance forecasting, which is the basis for PV power prediction. One day-ahead irradiance forecasts for single stations in Germany show a rRMSE of 36%. For regional forecasts, forecast accuracy is increasing in dependency on the size of the region. For the complete area of Germany, the rRMSE amounts to 13%. Besides the forecast accuracy, also the specification of the forecast uncertainty is an important issue for an effective application. We present and evaluate an approach to derive weather specific prediction intervals for irradiance forecasts. The accuracy of PV power prediction is investigated in a case study. read more

Topics:

Solar power (58%)58% related to the paper, Photovoltaic system (56%)56% related to the paper, Weather forecasting (54%)54% related to the paper,

637 Citations

Open access • Journal Article • DOI: 10.1109/JSTARS.2020.3005403 •

Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities

Gong Cheng¹, Xingxing Xie¹, •

29 Jun 2020 - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Abstract:

Remote sensing image scene classification, which aims at labeling remote sensing images with a set of semantic categories based on their contents, has broad applications in a range of fields. Propelled by the powerful feature learning capabilities of deep neural networks, remote sensing image scene classification driven by de... Remote sensing image scene classification, which aims at labeling remote sensing images with a set of semantic categories based on their contents, has broad applications in a range of fields. Propelled by the powerful feature learning capabilities of deep neural networks, remote sensing image scene classification driven by deep learning has drawn remarkable attention and achieved significant breakthroughs. However, to the best of our knowledge, a comprehensive review of recent achievements regarding deep learning for scene classification of remote sensing images is still lacking. Considering the rapid evolution of this field, this article provides a systematic survey of deep learning methods for remote sensing image scene classification by covering more than 160 papers. To be specific, we discuss the main challenges of remote sensing image scene classification and survey: first, autoencoder-based remote sensing image scene classification methods; second, convolutional neural network-based remote sensing image scene classification methods; and third, generative adversarial network-based remote sensing image scene classification methods. In addition, we introduce the benchmarks used for remote sensing image scene classification and summarize the performance of more than two dozen of representative algorithms on three commonly used benchmark datasets. Finally, we discuss the promising opportunities for further research. read more

Topics:

Feature learning (53%)53% related to the paper, Deep learning (52%)52% related to the paper, Autoencoder (51%)51% related to the paper,

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450 Citations

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13. What is Sherpa RoMEO Archiving Policy for IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing?

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. The table below indicates the level of access a journal has as per Sherpa Romeo's archiving policy.

RoMEO Colour	Archiving policy
Green	Can archive pre-print and post-print or publisher's version/PDF
Blue	Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow	Can archive pre-print (ie pre-refereeing)
White	Archiving not formally supported

FYI:

Pre-prints as being the version of the paper before peer review and
Post-prints as being the version of the paper after peer-review, with revisions having been made.

14. What are the most common citation types In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing?

The 5 most common citation types in order of usage for IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing are:.

S. No.	Citation Style Type
1.	Author Year
2.	Numbered
3.	Numbered (Superscripted)
4.	Author Year (Cited Pages)
5.	Footnote

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing — Template for authors

Related Journals

Journal Performance & Insights

Impact Factor

CiteRatio

3.827

7.2

Insights

Insights

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

1.246

1.579

Insights

Insights

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Top papers written in this journal

Abstract:

Topics:

Abstract:

Topics:

Abstract:

Topics:

Abstract:

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