Hypothesis Margin-Based Ensemble Method for the Classification of Noisy Remote Sensing Data-Reference-Cited by-同舟云学术

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Hypothesis Margin-Based Ensemble Method for the Classification of Noisy Remote Sensing Data

Published:2023 Issue: Volume:61 Page:1-21
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Feng Wei¹^ORCID,Gao Xinting¹,Boukir Samia²^ORCID,Xie Zhiwei¹,Quan Yinghui¹^ORCID,Huang Wenjiang³,Xing Mengdao⁴^ORCID

Affiliation:

1. Department of Remote Sensing Science and Technology, School of Electronic Engineering, Xidian University, Xi’an, China

2. CNRS, Bordeaux Institute of Technology (Bordeaux INP), IMB, UMR 5251, University of Bordeaux, Talence, France

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

4. Academy of Advanced Interdisciplinary Research, Xidian University, Xi’an, China

Funder

National Natural Science Foundation of China

Basic Research Program of Natural Sciences of Shaanxi Province

Shaanxi Forestry Science and Technology Innovation Key Project

Project of Shaanxi Federation of Social Sciences

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/10006360/10309993.pdf?arnumber=10309993

Reference52 articles.

1. A Training Data Set Cleaning Method by Classification Ability Ranking for the $k$ -Nearest Neighbor Classifier

2. Quality control and class noise reduction of satellite image time series

3. Relevance of airborne lidar and multispectral image data for urban scene classification using Random Forests

4. Learning From Mislabeled Training Data Through Ambiguous Learning for In-Home Health Monitoring

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1. A Stratified Mislabeled Instances Removal Method Based on Density Spatial Clustering for Hyperspectral Image Classification;IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium;2024-07-07

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