Title :
Function approximation to SAR image regions
Author_Institution :
Landcare Res. New Zealand, Lincoln, New Zealand
Abstract :
This article discusses function approximation to region surfaces in synthetic aperture radar (SAR) imagery. The need for function approximation is discussed from a signal estimation viewpoint in regard to noise filtering and edge detection. Assuming a multiplicative noise model and Gaussian statistics, a maximum likelihood (ML) criterion is formed for function approximation. Simulation results are used to compare the ML estimates of parameters with those of three least square (LS) criteria. The former is shown to be superior
Keywords :
function approximation; geophysical signal processing; geophysical techniques; image segmentation; maximum likelihood estimation; radar imaging; remote sensing by radar; synthetic aperture radar; Gaussian statistics; SAR image; edge detection; function approximation; geophysical measurement technique; image classification; image region analysis; image segmentation; land surface; maximum likelihood criterion; multiplicative noise model; noise filtering; radar imaging; radar remote sensing; region surface; signal estimation; synthetic aperture radar; terrain mapping; Filtering; Function approximation; Gaussian noise; Image edge detection; Least squares approximation; Maximum likelihood detection; Maximum likelihood estimation; Parameter estimation; Statistics; Synthetic aperture radar;
Conference_Titel :
Geoscience and Remote Sensing Symposium Proceedings, 1998. IGARSS '98. 1998 IEEE International
Conference_Location :
Seattle, WA
Print_ISBN :
0-7803-4403-0
DOI :
10.1109/IGARSS.1998.699705
