Title :
Smoothing SAR images with neural networks
Author :
Ellis, John ; Warner, Martin ; White, Richard G.
Author_Institution :
NA Software Ltd., Liverpool, UK
Abstract :
Describe an approach, to the removal of radar image noise, based on the use of neural networks. A neural network factorisation scheme, based on the use of vector quantisers, allows the authors to produce a more effective solution than that which is possible with a single network. The factorised neural network is currently trained to learn the smoothing behaviour of a noise removal algorithm. The success of the approach demonstrates the potential for this technique and opens the way for its use in learning a true noise smoothing mapping based on the comparison of single and multi look radar data
Keywords :
geophysical signal processing; geophysical techniques; image enhancement; multilayer perceptrons; radar applications; radar imaging; remote sensing; remote sensing by radar; smoothing methods; spaceborne radar; synthetic aperture radar; SAR; factorisation scheme; geophysical measurement technique; image noise removal; image processing; land surface; learning; multilayer perceptron; neural net; neural network; noise removal algorithm; radar imaging; remote sensing; smoothing; synthetic aperture radar; terrain mapping; vector quantiser; Airborne radar; Filters; Neural networks; Radar cross section; Radar imaging; Radar remote sensing; Scattering; Smoothing methods; Speckle; Synthetic aperture radar;
Conference_Titel :
Geoscience and Remote Sensing Symposium, 1994. IGARSS '94. Surface and Atmospheric Remote Sensing: Technologies, Data Analysis and Interpretation., International
Conference_Location :
Pasadena, CA
Print_ISBN :
0-7803-1497-2
DOI :
10.1109/IGARSS.1994.399601