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Learning Classifiers from Simulated ...

Bhalerao, Shridhar.

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Learning Classifiers from Simulated Satellite Data and Applications to Environmental Monitoring.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Learning Classifiers from Simulated Satellite Data and Applications to Environmental Monitoring./
Author:	Bhalerao, Shridhar.
Description:	61 p.
Notes:	Source: Masters Abstracts International, Volume: 51-06.
Contained By:	Masters Abstracts International51-06(E).
Subject:	Computer science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1538442
ISBN:	9781303115349

Learning Classifiers from Simulated Satellite Data and Applications to Environmental Monitoring.
Bhalerao, Shridhar.

Learning Classifiers from Simulated Satellite Data and Applications to Environmental Monitoring. - 61 p.

Source: Masters Abstracts International, Volume: 51-06.

Thesis (M.S.)--University of Maryland, Baltimore County, 2013.

Remote Sensing applications have gained a lot of attention in recent years due to their cost effectiveness over applications involving in-situ observations. We conduct experiments using the Community Radiative Transfer Model (CRTM) a widely used library for satellite observations to observe and detect high temperature points and concentrations of aerosols such as PM2.5 for a given region. The CRTM [1] is a simulation library which requires atmosphere and surface data in the form of gridded profiles and solves the radiative transfer problem in clear or scattered atmosphere. The observations/radiances of CRTM [1] assume microwave and infrared sensor. The observations/radiances by the sensor are in the form of channel brightness temperatures or radiances at multiple wavelengths. We simulate the atmosphere and surface conditions using the real whether data. The CRTM [1] performs a forward operation and calculates a vector of channel brightness temperature for the given atmosphere and surface conditions. We use the channel brightness data to build models to detect high temperature points and estimate the concentration of PM2.5 using classification and regression trees respectively. We test the models for different time periods and regions. The aim of the research is to create an observation operator using the CRTM [1] which can be used where observations of satellite are required to evaluate predictions made by some other simulation models.

ISBN: 9781303115349Subjects--Topical Terms:

523869
Computer science.

Learning Classifiers from Simulated Satellite Data and Applications to Environmental Monitoring.
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020 $a 9781303115349
035 $a (MiAaPQ)AAI1538442
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100 1 $a Bhalerao, Shridhar. $3 3184806
245 1 0 $a Learning Classifiers from Simulated Satellite Data and Applications to Environmental Monitoring.
300 $a 61 p.
500 $a Source: Masters Abstracts International, Volume: 51-06.
500 $a Adviser: Konstantinos Kalpakis.
502 $a Thesis (M.S.)--University of Maryland, Baltimore County, 2013.
520 $a Remote Sensing applications have gained a lot of attention in recent years due to their cost effectiveness over applications involving in-situ observations. We conduct experiments using the Community Radiative Transfer Model (CRTM) a widely used library for satellite observations to observe and detect high temperature points and concentrations of aerosols such as PM2.5 for a given region. The CRTM [1] is a simulation library which requires atmosphere and surface data in the form of gridded profiles and solves the radiative transfer problem in clear or scattered atmosphere. The observations/radiances of CRTM [1] assume microwave and infrared sensor. The observations/radiances by the sensor are in the form of channel brightness temperatures or radiances at multiple wavelengths. We simulate the atmosphere and surface conditions using the real whether data. The CRTM [1] performs a forward operation and calculates a vector of channel brightness temperature for the given atmosphere and surface conditions. We use the channel brightness data to build models to detect high temperature points and estimate the concentration of PM2.5 using classification and regression trees respectively. We test the models for different time periods and regions. The aim of the research is to create an observation operator using the CRTM [1] which can be used where observations of satellite are required to evaluate predictions made by some other simulation models.
520 $a Keywords: CRTM, Environment Monitoring, Classification Tree, Regression Tree.
590 $a School code: 0434.
650 4 $a Computer science. $3 523869
650 4 $a Remote sensing. $3 535394
650 4 $a Environmental management. $3 535182
690 $a 0984
690 $a 0799
690 $a 0474
710 2 $a University of Maryland, Baltimore County. $b Computer Science. $3 1018441
773 0 $t Masters Abstracts International $g 51-06(E).
790 $a 0434
791 $a M.S.
792 $a 2013
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1538442