Randomized Histogram Matching: A Simple Augmentation for Unsupervised Domain Adaptation in Overhead Imagery-Reference-Cited by-同舟云学术

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Randomized Histogram Matching: A Simple Augmentation for Unsupervised Domain Adaptation in Overhead Imagery

Published:2024 Issue: Volume:17 Page:1988-1998
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:

Yaras Can¹^ORCID,Kassaw Kaleb²^ORCID,Huang Bohao²^ORCID,Bradbury Kyle³^ORCID,Malof Jordan M.⁴^ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, University of Michigan, Ann Arbor, MI, USA

2. Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA

3. Nicholas Institute for Energy, Environment, and Sustainability, Duke University, Durham, NC, USA

4. Department of Computer Science, University of Montana, Missoula, MT, USA

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Atmospheric Science,Computers in Earth Sciences

Link

http://xplorestaging.ieee.org/ielx7/4609443/10330207/10348014.pdf?arnumber=10348014

Reference47 articles.

1. DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images

2. SpaceNet: A remote sensing dataset and challenge series;Etten,2018

3. Reduced focal loss: 1st place solution to xView object detection in satellite imagery;Sergievskiy,2019

4. ColorMapGAN: Unsupervised Domain Adaptation for Semantic Segmentation Using Color Mapping Generative Adversarial Networks

5. The Synthinel-1 dataset: a collection of high resolution synthetic overhead imagery for building segmentation

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