Hyperspectral Image Classification With Multi-Attention Transformer and Adaptive Superpixel Segmentation-Based Active Learning-Reference-Cited by-同舟云学术

Hyperspectral Image Classification With Multi-Attention Transformer and Adaptive Superpixel Segmentation-Based Active Learning

Published:2023 Issue: Volume:32 Page:3606-3621
ISSN:1057-7149
Container-title:IEEE Transactions on Image Processing
language:
Short-container-title:IEEE Trans. on Image Process.

Author:

Zhao Chunhui¹,Qin Boao¹,Feng Shou¹^ORCID,Zhu Wenxiang¹^ORCID,Sun Weiwei²^ORCID,Li Wei³^ORCID,Jia Xiuping⁴^ORCID

Affiliation:

1. College of Information and Communication Engineering and the Key Laboratory of Advanced Marine Communication and Information Technology, Ministry of Industry and Information Technology, Harbin Engineering University, Harbin, China

2. College of Architectural Engineering, Civil Engineering and Environment, Ningbo University, Ningbo, China

3. School of Information and Electronics, Beijing Institute of Technology, Beijing, China

4. School of Engineering and Information Technology, University of New South Wales, Canberra, ACT, Australia

Funder

National Natural Science Foundation of China

Open Fund of State Key Laboratory of Remote Sensing Science

Heilongjiang Provincial Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Graphics and Computer-Aided Design,Software

Link

http://xplorestaging.ieee.org/ielx7/83/9991910/10167502.pdf?arnumber=10167502

Reference68 articles.

1. Feature Extraction for Hyperspectral Imagery: The Evolution From Shallow to Deep: Overview and Toolbox

2. Superpixel-Guided Discriminative Low-Rank Representation of Hyperspectral Images for Classification

3. WHU-Hi: UAV-borne hyperspectral with high spatial resolution (H2) benchmark datasets and classifier for precise crop identification based on deep convolutional neural network with CRF

4. Hyperspectral Image Classification With Small Training Sample Size Using Superpixel-Guided Training Sample Enlargement

5. 3-D Deep Learning Approach for Remote Sensing Image Classification

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