Pseudo-Labeling Approach for Land Cover Classification Through Remote Sensing Observations With Noisy Labels-Reference-Cited by-同舟云学术

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Pseudo-Labeling Approach for Land Cover Classification Through Remote Sensing Observations With Noisy Labels

Published:2023 Issue: Volume:11 Page:82570-82583
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Mirpulatov Islombek¹^ORCID,Illarionova Svetlana¹^ORCID,Shadrin Dmitrii¹^ORCID,Burnaev Evgeny¹^ORCID

Affiliation:

1. Skolkovo Institute of Science and Technology (Skoltech), Moscow, Russia

Funder

Analytical Center through the Russian Federation (RF) Government

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10198463.pdf?arnumber=10198463

Reference62 articles.

1. Deep learning in remote sensing applications: A meta-analysis and review

2. Machine Learning in Python: Main Developments and Technology Trends in Data Science, Machine Learning, and Artificial Intelligence

3. A label noise tolerant random forest for the classification of remote sensing data based on outdated maps for training

4. “Zhores” — Petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in Skolkovo Institute of Science and Technology

5. Improving Deep Forest via Patch-Based Pooling, Morphological Profiling, and Pseudo Labeling for Remote Sensing Image Classification

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