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Improvements to Remote Sensing Algor...

Pachniak, Elliot.

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Improvements to Remote Sensing Algorithms Using Machine Learning Neural Networks.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Improvements to Remote Sensing Algorithms Using Machine Learning Neural Networks./
Author:	Pachniak, Elliot.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2024,
Description:	123 p.
Notes:	Source: Dissertations Abstracts International, Volume: 86-01, Section: B.
Contained By:	Dissertations Abstracts International86-01B.
Subject:	Remote sensing. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31146353
ISBN:	9798383181737

Improvements to Remote Sensing Algorithms Using Machine Learning Neural Networks.
Pachniak, Elliot.

Improvements to Remote Sensing Algorithms Using Machine Learning Neural Networks. - Ann Arbor : ProQuest Dissertations & Theses, 2024 - 123 p.

Source: Dissertations Abstracts International, Volume: 86-01, Section: B.

Thesis (Ph.D.)--Stevens Institute of Technology, 2024.

Modern satellite remote sensing plays a crucial role in providing data on various water, atmosphere, and land surface conditions. This research introduces improvements to remote sensing methods through a new method for quantifying measurement uncertainties in atmospheric correction algorithms of an existing tool for retrieval of aerosol and marine parameters from ocean color data (OC-SMART); an exploration of the impact of hyperspectral versus multispectral data channels on snow parameter retrieval algorithms; and applications of OC-SMART to Arctic water inherent optical property retrievals. Chapter 1 contains a background on remote sensing of environments; chapter 2 discusses critical tools used in this research; chapter 3 describes how to quantify uncertainties in OC-SMART using Bayesian inversion; chapter 4 explores the impact of hyperspectral information on retrievals of snow grain size and impurity concentration; chapter 5 discusses the application of OC-SMART to Arctic water inherent optical property retrievals; and chapter 6 summarizes the research and provides closing remarks.

ISBN: 9798383181737Subjects--Topical Terms:

535394
Remote sensing.
Subjects--Index Terms:

Modern satellite

Improvements to Remote Sensing Algorithms Using Machine Learning Neural Networks.
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245 1 0 $a Improvements to Remote Sensing Algorithms Using Machine Learning Neural Networks.
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300 $a 123 p.
500 $a Source: Dissertations Abstracts International, Volume: 86-01, Section: B.
500 $a Advisor: Stamnes, Knut.
502 $a Thesis (Ph.D.)--Stevens Institute of Technology, 2024.
520 $a Modern satellite remote sensing plays a crucial role in providing data on various water, atmosphere, and land surface conditions. This research introduces improvements to remote sensing methods through a new method for quantifying measurement uncertainties in atmospheric correction algorithms of an existing tool for retrieval of aerosol and marine parameters from ocean color data (OC-SMART); an exploration of the impact of hyperspectral versus multispectral data channels on snow parameter retrieval algorithms; and applications of OC-SMART to Arctic water inherent optical property retrievals. Chapter 1 contains a background on remote sensing of environments; chapter 2 discusses critical tools used in this research; chapter 3 describes how to quantify uncertainties in OC-SMART using Bayesian inversion; chapter 4 explores the impact of hyperspectral information on retrievals of snow grain size and impurity concentration; chapter 5 discusses the application of OC-SMART to Arctic water inherent optical property retrievals; and chapter 6 summarizes the research and provides closing remarks.
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650 4 $a Computer engineering. $3 621879
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653 $a Bayesian inversion
653 $a Ocean color data
653 $a Marine parameters
653 $a Machine learning
653 $a Neural networks
690 $a 0799
690 $a 0984
690 $a 0464
710 2 $a Stevens Institute of Technology. $b Physics / Polymer Engineering. $3 3276949
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