Results 1 to 10 of about 880,880 (163)

Correction: Li et al. Automatic Point Cloud Registration for Large Outdoor Scenes Using a Priori Semantic Information. Remote Sens. 2021, 13, 3474

Remote Sensing, 2022
In the original article [...]
Remote Sensing Editorial Office
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Acknowledgment to Reviewers of Remote Sensing in 2021

Remote Sensing, 2022
Rigorous peer-reviews are the basis of high-quality academic publishing [...]
Remote Sensing Editorial Office
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Correction: De Angeli et al. Newly Developed Tool for the Post-Processing of GPR Time-Slices in a GIS Environment. Remote Sens. 2022, 14, 3459

Remote Sensing, 2022
There was an error in the original publication [...]
Remote Sensing Editorial Office
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Correction: Lee, J.H.; Lindenschmidt, K.-E. Bias-Corrected RADARSAT-2 Soil Moisture Dynamics Reveal Discharge Hysteresis at an Agricultural Watershed. Remote Sens. 2023, 15, 2677

Remote Sensing, 2023
There was an error in the original publication [...]
Remote Sensing Editorial Office
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Announcement: Remote Sensing 2017 Best Guest Editor Award

Remote Sensing, 2018
Guest Editors help invite many high-quality papers for Remote Sensing[...]
Remote Sensing Office
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Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities [PDF]

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13: 3735-3756, 2020, 2020
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 ...
arxiv   +1 more source

Announcement: Remote Sensing 2017 Best Reviewer Award Winners

Remote Sensing, 2018
Peer review is an essential part of the publication process, ensuring that Remote Sensing maintains the high standard of its published papers[...]
Remote Sensing Office
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Acknowledgement to Reviewers of Remote Sensing in 2013

Remote Sensing, 2014
The publisher and editors of the Remote Sensing would like to express their sincere gratitude to the following reviewers for assessing manuscripts in 2013 for Remote Sensing.
Remote Sensing Editorial Office
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Acknowledgement to Reviewers of Remote Sensing in 2017

Remote Sensing, 2018
Peer review is an essential part in the publication process, ensuring that Remote Sensing maintains high quality standards for its published papers.[...]
Remote Sensing Editorial Office
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Acknowledgement to Reviewers of Remote Sensing in 2019

Remote Sensing, 2020
The editorial team greatly appreciates the reviewers who have dedicated their considerable time and expertise to the journal’s rigorous editorial process over the past 12 months, regardless of whether the papers are finally published or not.[...]
Remote Sensing Editorial Office
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