DocumentCode
298099
Title
Sensor data simulations using Monte-Carlo and neural network methods
Author
Kiang, Richard K.
Author_Institution
NASA Goddard Space Flight Center, Greenbelt, MD, USA
Volume
3
fYear
1996
fDate
27-31 May 1996
Firstpage
1864
Abstract
Parametric and non-parametric Monte-Carlo methods and a neural network method are used for data simulation. A Landsat-4 Thematic Mapper dataset and its ground truth are utilized for training and testing. The abilities and deficiencies of the three methods are compared. It is shown that the neural network method provides an attractive alternative for data simulation
Keywords
Monte Carlo methods; geophysical equipment; geophysical techniques; geophysics computing; neural nets; remote sensing; Landsat-4 Thematic Mapper; Monte Carlo method; geophysical measurement technique; land surface; multispectral remote sensing; neural net; neural network method; optical imaging; sensor data simulation; terrain mapping; testing; training; Atmospheric measurements; Atmospheric modeling; Covariance matrix; Gaussian distribution; Geophysical measurements; Neural networks; Remote sensing; Satellite broadcasting; Soil; Testing;
fLanguage
English
Publisher
ieee
Conference_Titel
Geoscience and Remote Sensing Symposium, 1996. IGARSS '96. 'Remote Sensing for a Sustainable Future.', International
Conference_Location
Lincoln, NE
Print_ISBN
0-7803-3068-4
Type
conf
DOI
10.1109/IGARSS.1996.516822
Filename
516822
Link To Document