RETRIEVAL OF SURFACE PARAMETERS USING DYNAMIC LEARNING NEURAL-NETWORK

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题名:	RETRIEVAL OF SURFACE PARAMETERS USING DYNAMIC LEARNING NEURAL-NETWORK
作者:	CHEN,KS;KAO,WL;TZENG,YC
贡献者:	太空及遙測研究中心
日期:	1995
上传时间:	2010-06-29 18:52:24 (UTC+8)
出版者:	中央大學
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.
關聯:	INTERNATIONAL JOURNAL OF REMOTE SENSING
显示于类别:	[太空及遙測研究中心] 期刊論文

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