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Title: Entropy and Boundary Based Adversarial Learning for Large Scale Unsupervised Domain Adaptation

Abstract

Supervised semantic segmentation methods provide state-of-the-art performance, but their performance is limited by the amount of quality labeled data they need for training. Scarcity of labeled data and non-transferablity of models, due to cross-domain discrepancy makes it a bigger challenge for remote sensing imagery analysis. In this work, we approach this problem through adversarial learning, driven by entropy and boundary of region-of-interest for unsupervised domain adaptation. This concept helps with better boundary prediction and encourages target domain entropy maps (probability/uncertainty maps) to be similar to source domains. In particular, we showed that deriving informative entropy through the adversarial learning is essential to enable the adaptation. We used a large scale cross country building extraction dataset to validate the framework. The experimental results show the usefulness of considering boundary and entropy driven adversarial learning for adaptation.

Authors:
 [1]; ORCiD logo [1]
  1. ORNL
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1777806
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Conference: IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2020) - Waikoloa, Hawaii, United States of America - 9/26/2020 4:00:00 PM-10/2/2020 4:00:00 PM
Country of Publication:
United States
Language:
English

Citation Formats

Makkar, Nikhil, and Yang, Lexie. Entropy and Boundary Based Adversarial Learning for Large Scale Unsupervised Domain Adaptation. United States: N. p., 2021. Web.
Makkar, Nikhil, & Yang, Lexie. Entropy and Boundary Based Adversarial Learning for Large Scale Unsupervised Domain Adaptation. United States.
Makkar, Nikhil, and Yang, Lexie. 2021. "Entropy and Boundary Based Adversarial Learning for Large Scale Unsupervised Domain Adaptation". United States. https://www.osti.gov/servlets/purl/1777806.
@article{osti_1777806,
title = {Entropy and Boundary Based Adversarial Learning for Large Scale Unsupervised Domain Adaptation},
author = {Makkar, Nikhil and Yang, Lexie},
abstractNote = {Supervised semantic segmentation methods provide state-of-the-art performance, but their performance is limited by the amount of quality labeled data they need for training. Scarcity of labeled data and non-transferablity of models, due to cross-domain discrepancy makes it a bigger challenge for remote sensing imagery analysis. In this work, we approach this problem through adversarial learning, driven by entropy and boundary of region-of-interest for unsupervised domain adaptation. This concept helps with better boundary prediction and encourages target domain entropy maps (probability/uncertainty maps) to be similar to source domains. In particular, we showed that deriving informative entropy through the adversarial learning is essential to enable the adaptation. We used a large scale cross country building extraction dataset to validate the framework. The experimental results show the usefulness of considering boundary and entropy driven adversarial learning for adaptation.},
doi = {},
url = {https://www.osti.gov/biblio/1777806}, journal = {},
number = ,
volume = ,
place = {United States},
year = {2021},
month = {2}
}

Conference:
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