Large Kernel Separable Mixed ConvNet for Remote Sensing Scene Classification-Reference-Cited by-同舟云学术

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Large Kernel Separable Mixed ConvNet for Remote Sensing Scene Classification

Published:2024 Issue: Volume:17 Page:4294-4303
ISSN:1939-1404
Container-title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
language:
Short-container-title:IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Author:

Zhang Keqian¹^ORCID,Cui Tengfei²^ORCID,Wu Wei³^ORCID,Zheng Xueke¹^ORCID,Cheng Gang¹^ORCID

Affiliation:

1. College of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, China

2. Metropolitan College, Boston University, Boston, MA, USA

3. College of Geomatics and Geoinformation, Guilin University of Technology, Guilin, China

Funder

National Key R&D Program Projects

National Natural Science Foundation of China

China Postdoctoral Science Foundation

Special Funding for Basic Scientific Research Business Expenses of Henan Universities

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/4609443/10330207/10399773.pdf?arnumber=10399773

Reference74 articles.

1. A Survey on Deep Learning-Based Change Detection from High-Resolution Remote Sensing Images

2. Classifying urban land use by integrating remote sensing and social media data

3. Exploring semantic elements for urban scene recognition: Deep integration of high-resolution imagery and OpenStreetMap (OSM)

4. Urban scene understanding based on semantic and socioeconomic features: From high-resolution remote sensing imagery to multi-source geographic datasets

5. Identifying Urban Wetlands through Remote Sensing Scene Classification Using Deep Learning: A Case Study of Shenzhen, China

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