RETRIEVAL OF SURFACE PARAMETERS USING DYNAMIC LEARNING NEURAL-NETWORK

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Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/27694

Title:	RETRIEVAL OF SURFACE PARAMETERS USING DYNAMIC LEARNING NEURAL-NETWORK
Authors:	CHEN,KS;KAO,WL;TZENG,YC
Contributors:	太空及遙測研究中心
Date:	1995
Issue Date:	2010-06-29 18:52:24 (UTC+8)
Publisher:	中央大學
Abstract:	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.
Relation:	INTERNATIONAL JOURNAL OF REMOTE SENSING
Appears in Collections:	[Center for Space and Remote Sensing Research ] journal & Dissertation

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