期刊荟萃|IEEE Geoscience and Remote Sensing Letters

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Aims & Scope

IEEE Geoscience and Remote Sensing Letters (GRSL) is a monthly publication for short papers (maximum length 5 pages) addressing new ideas and formative concepts in remote sensing as well as important new and timely results and concepts. Papers should relate to 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. The technical content of papers must be both new and significant. Experimental data must be complete and include sufficient description of experimental apparatus, methods, and relevant experimental conditions. GRSL encourages the incorporation of "extended objects" or "multimedia" such as animations to enhance the shorter papers.

Journal Information

Impact Factor：2.761

Eigenfactor：0.01984

(Eigenfactor™ Score takes into account the number of times articles from a journal published in the last five years have been cited in the JCR year while also considering which journals have contributed these citations. Since citations are in this case weighted dependently on the source, The Eigenfactor Score belongs to the class of so-called prestige measures. The Eigenfactor Score represents the probability of reading a specific journal in the entire collection and therefore high-scoring journals have a greater influence in the scientific community.)

Article Influence Score：0.799

(Article Influence™ Score is also a prestige measure and has all the features of the Eigenfactor Score, with an additional normalization to the number of published papers. Hence it can be considered the average influence of a journal's articles over the first five years after publication.)

Frequency：12

ISSN：12

Published by: IEEE Geoscience and Remote Sensing Society

Subjects: Geoscience

Latest Published Articles

Evaluation of the Multilook Size in Polarimetric Optimization of Differential SAR Interferograms

Semantic Labeling Using a Low-Power Neuromorphic Platform

A Segmentation Method for High Spatial Resolution Remote Sensing Images Based on the Fusion of Multifeatures

Erratum to `Multiscale Fully Convolutional Network for Foreground Object Detection in Infrared Videos'

Background Context-Aware-Based SAR Image Saliency Detection

Popular Articles

Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data

Vehicle Detection in Satellite Images by Hybrid Deep Convolutional Neural Networks

A Systematic Approach for Variable Selection With Random Forests: Achieving Stable Variable Importance Values

Deep Learning Based Feature Selection for Remote Sensing Scene Classification

Fully Convolutional Networks for Semantic Segmentation of Very High Resolution Remotely Sensed Images Combined With DSM

Official Website

https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=8859

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