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IEEE Geoscience and Remote Sensing Letters is a peer-reviewed journal published by IEEE, covering Remote-Sensing Image Classification, Advanced SAR Imaging Techniques, Synthetic Aperture Radar (SAR) Applications and Techniques. Over its lifetime it has published 11,746 papers, which have received 285,198 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 Letters, vol. 12, no. 3, pp. 45–58, 2023.
Formats any DOI in IEEE Geoscience and Remote Sensing Letters style. No sign-up. |
| Abbreviation (ISO-4) | GRSL |
| Publishes research in | Remote-Sensing Image Classification Advanced SAR Imaging Techniques Synthetic Aperture Radar (SAR) Applications and Techniques Remote Sensing and Land Use Advanced Image Fusion Techniques |
| ISSN | 1545-598X |
| Citation impact (2-yr) | 3.52 |
| h-index | 177 |
| i10-index | 5,853 |
| 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.
Road extraction from aerial images has been a hot research topic in the field of remote sensing image analysis. In this letter, a semantic segmentation neural network, which combines the strengths of residual learning and U-Net, is proposed for road area extraction. The network is built with residual units and has similar architecture to that…
Hyperspectral image (HSI) classification is widely used for the analysis of remotely sensed images. Hyperspectral imagery includes varying bands of images. Convolutional neural network (CNN) is one of the most frequently used deep learning-based methods for visual data processing. The use of CNN for HSI classification is also visible in recent works. These approaches are…
Deep learning (DL) is a powerful state-of-the-art technique for image processing including remote sensing (RS) images. This letter describes a multilevel DL architecture that targets land cover and crop type classification from multitemporal multisource satellite imagery. The pillars of the architecture are unsupervised neural network (NN) that is used for optical imagery segmentation and missing…
The Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) at the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center has processed and released 2100 Landsat Thematic Mapper and Enhanced Thematic Mapper Plus surface reflectance scenes, providing 30-m resolution wall-to-wall reflectance coverage for North America for epochs centered on 1990 and 2000. This dataset can…
In this letter, we propose a novel technique for unsupervised change detection in multitemporal satellite images using principal component analysis (PCA) and k-means clustering. The difference image is partitioned into h times h nonoverlapping blocks. S, S les h <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , orthonormal eigenvectors are extracted through PCA of h times h nonoverlapping block…
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