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

详细信息   

• 作者：Bhalerao ; Shridhar.
• 学历：M.S.
• 年：2013
• 关键词：CRTM ; Environment Monitoring ; Classification Tree
• 导师：Kalpakis, Konstantinos,eadvisorKalpakis, Konstantinosecommittee memberPhatak, Dhananjayecommittee memberPeng, Yeunecommittee member
• 毕业院校：University of Maryland
• Department：Computer Science
• ISBN：9781303115349
• CBH：1538442
• Country：USA
• 语种：English
• FileSize：1703104
• Pages：61

文摘

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.