Evaluation Of A Neural Network For Formulating A Semi-Empirical Variable Kernel Brdf Model

Author

Madhu Manoharan

Issuing Body

Mississippi State University

Advisor

King, Roger L.

Committee Member

Younan, Nicholas H.

Committee Member

Luke, Edward A.

Date of Degree

5-7-2005

Original embargo terms

MSU Only Indefinitely

Document Type

Graduate Thesis - Campus Access Only

Major

Computer Engineering

Degree Name

Master of Science

College

James Worth Bagley College of Engineering

Department

Department of Electrical and Computer Engineering

Abstract

To understand remotely sensed data, one must understand the relationship between radiative transfer models and their predictions of the interaction of solar radiation on geophysical media. If it can be established that these models are indeed accurate, some form of evaluation has to be performed on these models, for users to choose the model that suits their requirements. This thesis focuses on the implementation of a variable linear kernel model, its validation, and to study its application in the prediction of BRDF effects using two different neural networks-- the backpropogation and the radial basis function neural network and finally to draw conclusions on which neural network is best suited for this model. Based on these results the optimum number of kernels for this model is derived.

URI

https://hdl.handle.net/11668/17456

Recommended Citation

Manoharan, Madhu, "Evaluation Of A Neural Network For Formulating A Semi-Empirical Variable Kernel Brdf Model" (2005). Theses and Dissertations. 1991.
https://scholarsjunction.msstate.edu/td/1991