Fuzzy Similarity Analysis of Effective Training Samples to Improve Machine Learning Estimations of Water Quality Parameters Using Sentinel-2 Remote Sensing Data-Reference-Cited by-同舟云学术

Fuzzy Similarity Analysis of Effective Training Samples to Improve Machine Learning Estimations of Water Quality Parameters Using Sentinel-2 Remote Sensing Data

Published:2024 Issue: Volume:17 Page:5121-5136
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:

Dehkordi Alireza Taheri¹^ORCID,Zoej Mohammad Javad Valadan¹^ORCID,Mehran Ali²^ORCID,Jafari Mohsen³^ORCID,Chegoonian Amir Masoud⁴^ORCID

Affiliation:

1. Department of Photogrammetry and Remote Sensing, Faculty of Geomatics Engineering, K. N. Toosi University of Technology, Tehran, Iran

2. Department of Civil and Environmental Engineering, San Jose State University, San Jose, CA, USA

3. Department of Civil and Environmental Engineering, School of Engineering, Shiraz University, Shiraz, Iran

4. Department of Geography and Environmental Management, University of Waterloo, Waterloo, ON, Canada

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/4609443/10330207/10430087.pdf?arnumber=10430087

Reference76 articles.

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5. An advanced remote sensing retrieval method for urban non-optically active water quality parameters: An example from Shanghai

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