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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/28036

Title: An Efficient Hierarchical Hyperspectral Image Classification Using Binary Quaternion-Moment-Preserving Thresholding Technique
Authors: Lena Chang;Ching-Min Cheng;Yang-Lang Chang
Contributors: NTOU:Department of Communications Navigation and Control Engineering
國立臺灣海洋大學:通訊與導航工程學系
Date: 2009-07-12
Issue Date: 2011-10-21T02:35:39Z
Publisher: Geoscience and Remote Sensing Symposium,2009 IEEE International,IGARSS 2009
Abstract: Abstract:In the study, we propose a novel unsupervised classification technique for hyperspectral images, which consists of two algorithms, referred to as the maximum correlation band clustering (MCBC) and hierarchical binary quaternionmoment-preserving (BQMP) thresholding technique. By the MCBC, we partition the bands into groups and transfer the high-dimensional image data into low-dimensional image features. Afterwards, the hierarchical BQMP approach partitions the feature image into proper regions according to the spectral characteristics. Simulation results performed on AVIRIS images have demonstrated the efficiency of the proposed approaches.
Relation: 2, pp.294-297
URI: http://ntour.ntou.edu.tw/handle/987654321/28036
Appears in Collections:[通訊與導航工程學系] 演講及研討會

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