Self-Filtered Learning for Semantic Segmentation of Buildings in Remote Sensing Imagery With Noisy Labels-Reference-Cited by-同舟云学术

Self-Filtered Learning for Semantic Segmentation of Buildings in Remote Sensing Imagery With Noisy Labels

Published:2023 Issue: Volume:16 Page:1113-1129
ISSN:1939-1404
Container-title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
language:
Short-container-title:IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Author:

Song Hunsoo¹^ORCID,Yang Lexie²^ORCID,Jung Jinha²^ORCID

Affiliation:

1. Lyles School of Civil Engineering, Purdue University, West Lafayette, IN, USA

2. National Security Sciences Directorate, Oak Ridge National Laboratory, Oak Ridge, TN, USA

Funder

Frederick N. Andrews Fellowship

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Atmospheric Science,Computers in Earth Sciences

Link

Reference75 articles.

4. Understanding and utilizing deep neural networks trained with noisy labels;chen;Proc Int Conf Mach Learn,2019

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