Superpixelwise Low-Rank Approximation-Based Partial Label Learning for Hyperspectral Image Classification-Reference-Cited by-同舟云学术

Superpixelwise Low-Rank Approximation-Based Partial Label Learning for Hyperspectral Image Classification

Published:2023 Issue: Volume:20 Page:1-5
ISSN:1545-598X
Container-title:IEEE Geoscience and Remote Sensing Letters
language:
Short-container-title:IEEE Geosci. Remote Sensing Lett.

Author:

Yang Shujun¹^ORCID,Zhang Yu¹^ORCID,Ding Yao²^ORCID,Hong Danfeng³^ORCID

Affiliation:

1. Department of Computer Science and Engineering, Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation, Southern University of Science and Technology, Shenzhen, China

2. School of Optical Engineering, Xi’an Research Institute of High Technology, Xi’an, China

3. Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Geotechnical Engineering and Engineering Geology

Link

http://xplorestaging.ieee.org/ielx7/8859/10034981/10136223.pdf?arnumber=10136223

Reference21 articles.

1. Spectral–Spatial Classification of Hyperspectral Image Based on Low-Rank Decomposition

2. Solving the partial label learning problem: An instance-based approach;zhang;Proc IJCAI,2015

3. Entropy rate superpixel segmentation

4. Hyperspectral image denoising with superpixel segmentation and low-rank representation

5. A New Spatial–Spectral Feature Extraction Method for Hyperspectral Images Using Local Covariance Matrix Representation

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