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IEEE Geoscience and Remote Sensing Magazine is a peer-reviewed journal published by IEEE, covering Diverse Scientific and Economic Studies, Remote-Sensing Image Classification, Human auditory perception and evaluation. Over its lifetime it has published 1,323 papers, which have received 37,185 citations.
| Publisher | IEEE |
|---|---|
| Reference style | Numbered (IEEE) Numbered — [1], [2] in the text [1] A. Smith, B. Jones, and C. Lee, "A representative article title," IEEE Geoscience and Remote Sensing Magazine, vol. 12, no. 3, pp. 45–58, 2023.
Formats any DOI in IEEE Geoscience and Remote Sensing Magazine style. No sign-up. |
| Abbreviation (ISO-4) | GRSM |
| Publishes research in | Diverse Scientific and Economic Studies Remote-Sensing Image Classification Human auditory perception and evaluation Synthetic Aperture Radar (SAR) Applications and Techniques Educational Robotics and Engineering |
| ISSN | 2168-6831 |
| Citation impact (2-yr) | 5.9 |
| h-index | 83 |
| i10-index | 263 |
| Top institutions publishing here | Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR) |
| Journal website | ieeexplore.ieee.org |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Central to the looming paradigm shift toward data-intensive science, machine-learning techniques are becoming increasingly important. In particular, deep learning has proven to be both a major breakthrough and an extremely powerful tool in many fields. Shall we embrace deep learning as the key to everything? Or should we resist a black-box solution? These are controversial…
Synthetic Aperture Radar (SAR) has been widely used for Earth remote sensing for more than 30 years. It provides high-resolution, day-and-night and weather-independent images for a multitude of applications ranging from geoscience and climate change research, environmental and Earth system monitoring, 2-D and 3-D mapping, change detection, 4-D mapping (space and time), security-related applications up…
Deep-learning (DL) algorithms, which learn the representative and discriminative features in a hierarchical manner from the data, have recently become a hotspot in the machine-learning area and have been introduced into the geoscience and remote sensing (RS) community for RS big data analysis. Considering the low-level features (e.g., spectral and texture) as the bottom level,…
Hyperspectral remote sensing technology has advanced significantly in the past two decades. Current sensors onboard airborne and spaceborne platforms cover large areas of the Earth surface with unprecedented spectral, spatial, and temporal resolutions. These characteristics enable a myriad of applications requiring fine identification of materials or estimation of physical parameters. Very often, these applications rely…
Recent advances in airborne and spaceborne hyperspectral imaging technology have provided end users with rich spectral, spatial, and temporal information. They have made a plethora of applications feasible for the analysis of large areas of the Earth?s surface. However, a significant number of factors-such as the high dimensions and size of the hyperspectral data, the…
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