Retrieval of surface parameters using dynamic learning neural network
作者:
K. S. CHEN,
W. L. KAO,
Y. C. TZENG,
期刊:
International Journal of Remote Sensing
(Taylor Available online 1995)
卷期:
Volume 16,
issue 5
页码: 801-809
ISSN:0143-1161
年代: 1995
DOI:10.1080/01431169508954444
出版商: Taylor & Francis Group
数据来源: Taylor
摘要:
A highly dynamic learning (DL) neural network is developed and applied to perform the inversion of rough surface parameters: dielectric constant, surface rms height, and correlation length, The network training scheme is based on the Kalman filter technique which lends itself to a highly dynamic and adaptive merit during the learning stage. The training data sets utilized were obtained from the Integral Equation Model (IEM) which has a wide range of frequency. The training speed of the network is found to be much faster than the back-propagation (BP) trained multi-layer preceptron (MLP) with the same degree of accuracy. When applied to invert the surface parameters, the DL network shows a very satisfactory result in terms of learning time and process accuracy which thus enhances its potential applications to remote sensing of rough surfaces.
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