Multimodal Collaboration Networks for Geospatial Vehicle Detection in Dense, Occluded, and Large-Scale Events-Reference-Cited by-同舟云学术

Multimodal Collaboration Networks for Geospatial Vehicle Detection in Dense, Occluded, and Large-Scale Events

Published:2024 Issue: Volume:62 Page:1-12
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Wu Xin¹^ORCID,Huang Zhanchao²^ORCID,Wang Li¹^ORCID,Chanussot Jocelyn³^ORCID,Tian Jiaojiao⁴^ORCID

Affiliation:

1. School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing, China

2. Key Laboratory of Spatial Data Mining and Information Sharing of the Ministry of Education, The Academy of Digital China, and the National and Local Joint Engineering Research Center of Satellite Geospatial Information Technology, Fuzhou University, Fuzhou, China

3. University of Grenoble Alpes, CNRS, Grenoble INP, GIPSA-Lab, Grenoble, France

4. Remote Sensing Technology Institute, German Aerospace Center, Weßling, Germany

Funder

National Natural Science Foundation of China

Natural Science Foundation of Beijing Municipality

MIAI@Grenoble Alpes

Scientific Research Startup Fundation of Fuzhou University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/36/10354519/10475352.pdf?arnumber=10475352

Reference31 articles.

1. Deep Learning for Unmanned Aerial Vehicle-Based Object Detection and Tracking: A Survey

2. SpectralGPT: Spectral Remote Sensing Foundation Model

3. Artificial and beneficial – Exploiting artificial images for aerial vehicle detection

4. UIU-Net: U-Net in U-Net for Infrared Small Object Detection

5. ORSIm Detector: A Novel Object Detection Framework in Optical Remote Sensing Imagery Using Spatial-Frequency Channel Features

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