Improving Classification of Remotely Sensed Images with the Swin Transformer-Reference-Cited by-同舟云学术

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Improving Classification of Remotely Sensed Images with the Swin Transformer

Published:2022-03-26 Issue: Volume: Page:
ISSN:
Container-title:SoutheastCon 2022
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Author:

Jannat Fatema-E¹,Willis Andrew R.¹

Affiliation:

1. University of North Carolina at Charlotte,Dept. of Electrical and Computer Engineering,Charlotte,North Carolina,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9763896/9763889/09764016.pdf?arnumber=9764016

Reference43 articles.

1. CvT: Introducing Convolutions to Vision Transformers

2. Scene-former: Indoor scene generation with transformers;wang;CoRR,2020

3. Training data-efficient image transformers & distillation through attention;touvron;CoRR,2020

4. Efficientnet: Rethinking model scaling for convolutional neural networks;tan;CoRR,2019

5. Going deeper with convolutions;szegedy;CoRR,2014

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1. Transformer-based land use and land cover classification with explainability using satellite imagery;Scientific Reports;2024-07-20

2. Effect of intra-year Landsat scene availability in land cover land use classification in the conterminous United States using deep neural networks;ISPRS Journal of Photogrammetry and Remote Sensing;2024-06

3. Pediatric oral health detection using Swin transformer;2024 Third International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE);2024-04-26

4. PVswin-YOLOv8s: UAV-Based Pedestrian and Vehicle Detection for Traffic Management in Smart Cities Using Improved YOLOv8;Drones;2024-02-28

5. A Comparative Study of Swin-Based Enhanced Remote Sensing Image Classifications;Proceedings of the 2024 6th International Conference on Image Processing and Machine Vision;2024-01-12

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