Mapping multi-spectral remote sensing images using rule extraction approach

NCUIR > Engineering College > Graduate Institute of construction engineering > journal & Dissertation > Item 987654321/50085

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/50085

Title:	Mapping multi-spectral remote sensing images using rule extraction approach
Authors:	Su,MC;Huang,DY;Chen,JH;Lu,WZ;Tsai,LC;Lin,JZ
Contributors:	營建管理研究所
Keywords:	SIMPLIFIED FUZZY ARTMAP;LAND-USE CLASSIFICATION;NEURAL-NETWORK;RECOGNITION;SYSTEMS
Date:	2011
Issue Date:	2012-03-27 17:03:06 (UTC+8)
Publisher:	國立中央大學
Abstract:	To improve the accurate rate of mapping multi-spectral remote sensing images, in this paper we construct a class of HyperRectangular Composite Neural Networks (HRCNNs), integrating the paradigms of neural networks with the rule-based approach. The supervised decision-directed learning (SDDL) algorithm is also adopted to construct a two-layer network in a sequential manner by adding hidden nodes as needed. Thus, the classification knowledge embedded in the numerical weights of trained HRCNNs can be extracted and represented in the form of If-Then rules. The rules facilitate justification on the responses to increase accuracy of the classification. A sample of remote sensing image containing forest land, river, dam area, and built-up land is used to examine the proposed approach. The accurate recognition rate reaching over 99% demonstrates that the proposed approach is capable of dealing with image mapping. (C) 2011 Elsevier Ltd. All rights reserved.
Relation:	EXPERT SYSTEMS WITH APPLICATIONS
Appears in Collections:	[Graduate Institute of construction engineering] journal & Dissertation

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	728	View/Open

社群 sharing