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IEEE Transactions on Geoscience and Remote Sensing is a peer-reviewed journal published by IEEE, covering Remote-Sensing Image Classification, Synthetic Aperture Radar (SAR) Applications and Techniques, Soil Moisture and Remote Sensing. Over its lifetime it has published 27,045 papers, which have received 1,186,157 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 Transactions on Geoscience and Remote Sensing, vol. 12, no. 3, pp. 45–58, 2023.
Formats any DOI in IEEE Transactions on Geoscience and Remote Sensing style. No sign-up. |
| Abbreviation (ISO-4) | TGRS |
| Publishes research in | Remote-Sensing Image Classification Synthetic Aperture Radar (SAR) Applications and Techniques Soil Moisture and Remote Sensing Advanced SAR Imaging Techniques Remote Sensing and Land Use |
| ISSN | 0196-2892 |
| Citation impact (2-yr) | 6.21 |
| h-index | 382 |
| i10-index | 15,610 |
| Top institutions publishing here | Chinese Academy of Sciences |
| 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.
Temporal and geometrical decorrelation often prevents SAR interferometry from being an operational tool for surface deformation monitoring and topographic profile reconstruction. Moreover, atmospheric disturbances can strongly compromise the accuracy of the results. The authors present a complete procedure for the identification and exploitation of stable natural reflectors or permanent scatterers (PSs) starting from long temporal…
We present a new differential synthetic aperture radar (SAR) interferometry algorithm for monitoring the temporal evolution of surface deformations. The presented technique is based on an appropriate combination of differential interferograms produced by data pairs characterized by a small orbital separation (baseline) in order to limit the spatial decorrelation phenomena. The application of the singular…
This paper addresses the problem of the classification of hyperspectral remote sensing images by support vector machines (SVMs). First, we propose a theoretical discussion and experimental analysis aimed at understanding and assessing the potentialities of SVM classifiers in hyperdimensional feature spaces. Then, we assess the effectiveness of SVMs with respect to conventional feature-reduction-based approaches and…
Remote sensing from satellite or airborne platforms of land or sea surfaces in the visible and near infrared is strongly affected by the presence of the atmosphere along the path from Sun to target (surface) to sensor. This paper presents 6S (Second Simulation of the Satellite Signal in the Solar Spectrum), a computer code which…
Due to the advantages of deep learning, in this paper, a regularized deep feature extraction (FE) method is presented for hyperspectral image (HSI) classification using a convolutional neural network (CNN). The proposed approach employs several convolutional and pooling layers to extract deep features from HSIs, which are nonlinear, discriminant, and invariant. These features are useful…
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