CNN and Transformer interaction network for hyperspectral image classification-Reference-Cited by-同舟云学术

CNN and Transformer interaction network for hyperspectral image classification

Published:2023-09-08 Issue:18 Volume:44 Page:5548-5573
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Li Zhongwei¹,Huang Wenhao¹,Wang Leiquan²,Xin Ziqi¹,Meng Qiao²

Affiliation:

1. College of Oceanography and Space Informatics, China University of Petroleum, Qingdao, China

2. Qingdao institute of Software, College of computer science and technology, China University of Petroleum, Qingdao, China

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2023.2249598

Reference33 articles.

1. Tri-CNN: A Three Branch Model for Hyperspectral Image Classification

2. SpectralSWIN: a spectral-swin transformer network for hyperspectral image classification

3. Evaluation of Deep Learning CNN Model for Land Use Land Cover Classification and Crop Identification Using Hyperspectral Remote Sensing Images

4. Classification for hyperspectral imagery based on sparse representation

5. Dosovitskiy A. L. Beyer A. Kolesnikov D. Weissenborn X. Zhai T. Unterthiner M. Dehghani et al. 2020. “An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.” arXiv preprint arXiv:2010.11929

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