Do We Need Learnable Classifiers? A Hyperspectral Image Classification Algorithm Based on Attention-Enhanced ResBlock-in-ResBlock and ETF Classifier-Reference-Cited by-同舟云学术

Do We Need Learnable Classifiers? A Hyperspectral Image Classification Algorithm Based on Attention-Enhanced ResBlock-in-ResBlock and ETF Classifier

Published:2024 Issue: Volume:62 Page:1-13
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Fu Chuan¹^ORCID,Du Bo²^ORCID,Zhang Liangpei¹^ORCID

Affiliation:

1. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China

2. Hubei Key Laboratory of Multimedia and Network Communication Engineering, School of Computer Science, National Engineering Research Center for Multimedia Software, Institute of Artificial Intelligence, Wuhan University, Wuhan, China

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/36/10354519/10398227.pdf?arnumber=10398227

Reference56 articles.

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2. Generalized Scene Classification From Small-Scale Datasets With Multitask Learning

3. Boltzmann Entropy-Based Unsupervised Band Selection for Hyperspectral Image Classification

4. A Band Subset Selection Approach Based on Sparse Self-Representation and Band Grouping for Hyperspectral Image Classification

5. PCA-Based Edge-Preserving Features for Hyperspectral Image Classification

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