An Adaptive Thresholding Multiple Classifiers System for Remote Sensing Image Classification

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Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/27646

Title:	An Adaptive Thresholding Multiple Classifiers System for Remote Sensing Image Classification
Authors:	Tzeng,YC;Fan,KT;Chen,KS
Contributors:	太空及遙測研究中心
Keywords:	LEARNING NEURAL-NETWORK;FUSION
Date:	2009
Issue Date:	2010-06-29 18:51:21 (UTC+8)
Publisher:	中央大學
Abstract:	A multiple classifiers system which adopts an effective weighting policy to combine the output of several classifiers, generally leads to a better performance in image classification. The two most commonly used weighting policies are Bagging and Boosting algorithms. However, their performance is limited by high levels of ambiguity among classes. To overcome this difficulty, an adaptive thresholding criterion was proposed. By applying it to SAR and optical images for terrain cover classification, comparisons between the multiple classifiers systems using the Bagging and/or Boosting algorithms with and without the adaptive thresholding criterion were made. Experimental results showed that the classification substantially improved when the adaptive thresholding criterion was used, especially when the level of ambiguity of targets was high.
Relation:	PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING
Appears in Collections:	[Center for Space and Remote Sensing Research ] journal & Dissertation

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