Graph-Based Active Learning for Surface Water and Sediment Detection in Multispectral Images-Reference-Cited by-同舟云学术

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Graph-Based Active Learning for Surface Water and Sediment Detection in Multispectral Images

Published:2023-07-16 Issue: Volume: Page:
ISSN:
Container-title:IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium
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Author:

Chen Bohan¹,Miller Kevin²,Bertozzi Andrea L.¹,Schwenk Jon³

Affiliation:

1. University of California,Los Angeles. Math Dept.,Los Angeles,CA,USA,90095

2. University of Texas,Austin. Oden Institute for Computational Engineering and Sciences,Austin,TX,USA,78712

3. Los Alamos National Laboratory,Earth & Env. Sci. Division,NM,USA,87545

Funder

National Geospatial-Intelligence Agency

Research and Development

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10281394/10281399/10282009.pdf?arnumber=10282009

Reference18 articles.

1. Efficient graph-based active learning with probit likelihood via Gaussian approximations;miller;ICML Workshop on Experimental Design and Active Learning,2020

2. Semi-supervised learning using Gaussian fields and harmonic functions;zhu;Proceedings of the 20th International Conference on Machine Learning (ICML-03),2003

3. Batch Active Learning for Multispectral and Hyperspectral Image Segmentation Using Similarity Graphs

4. Model-change active learning in graph-based semi-supervised learning;miller,2021

5. Graph-based active learning for semi-supervised classification of SAR data

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