RSMamba: Remote Sensing Image Classification With State Space Model-Reference-Cited by-同舟云学术

RSMamba: Remote Sensing Image Classification With State Space Model

Published:2024 Issue: Volume:21 Page:1-5
ISSN:1545-598X
Container-title:IEEE Geoscience and Remote Sensing Letters
language:
Short-container-title:IEEE Geosci. Remote Sensing Lett.

Author:

Chen Keyan¹^ORCID,Chen Bowen¹^ORCID,Liu Chenyang¹^ORCID,Li Wenyuan²,Zou Zhengxia³^ORCID,Shi Zhenwei¹^ORCID

Affiliation:

1. Image Processing Center, School of Astronautics, and the State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China

2. Department of Geography, The University of Hong Kong, Hong Kong, China

3. Shanghai Artificial Intelligence Laboratory, Shanghai, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Beijing Natural Science Foundation

Fundamental Research Funds for the Central Universities

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/8859/10365397/10542538.pdf?arnumber=10542538

Reference19 articles.

1. Resolution-Agnostic Remote Sensing Scene Classification With Implicit Neural Representations

2. AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification

3. Bag-of-visual-words and spatial extensions for land-use classification

4. Remote Sensing Image Scene Classification: Benchmark and State of the Art

5. Building Extraction from Remote Sensing Images with Sparse Token Transformers

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