Unsupervised Classification of Remote Sensing Imagery With Non-negative Matrix Factorization

碩士 === 國立臺灣大學 === 資訊工程學研究所 === 93 === An unsupervised classification method provides the interpretation, feature extraction and endmember estimation for the remote sensing image data without any prior knowledge about the ground quality. We explore such method and construct an algorithm based on the...

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Bibliographic Details
Main Authors:	Kuang-De, Ou Yang, 歐陽廣德
Other Authors:	Cheng-yuan Liou
Format:	Others
Language:	en_US
Published:	2005
Online Access:	http://ndltd.ncl.edu.tw/handle/14790420395701310054

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spelling	ndltd-TW-093NTU053920162015-12-21T04:04:53Z http://ndltd.ncl.edu.tw/handle/14790420395701310054 Unsupervised Classification of Remote Sensing Imagery With Non-negative Matrix Factorization 應用NMF方法分析多頻譜遙測影像 Kuang-De, Ou Yang 歐陽廣德碩士國立臺灣大學資訊工程學研究所 93 An unsupervised classification method provides the interpretation, feature extraction and endmember estimation for the remote sensing image data without any prior knowledge about the ground quality. We explore such method and construct an algorithm based on the non-negative matrix factorization (NMF). The use of the NMF is to match the non-negative property in sensing spectrum data.. The data dimensionality is estimated by using the partitioned noise-adjusted principlal component analysis (PNAPCA). The initial matrix used to start the NMF is obtained by using the fuzzy c-mean (FCM). This algorithm is capable to produce a region- or part-based representation of objects in images. Both simulated and real sensing data are used to test the algorithm. Cheng-yuan Liou 劉長遠 2005 學位論文 ; thesis 43 en_US
collection	NDLTD
language	en_US
format	Others
sources	NDLTD
description	碩士 === 國立臺灣大學 === 資訊工程學研究所 === 93 === An unsupervised classification method provides the interpretation, feature extraction and endmember estimation for the remote sensing image data without any prior knowledge about the ground quality. We explore such method and construct an algorithm based on the non-negative matrix factorization (NMF). The use of the NMF is to match the non-negative property in sensing spectrum data.. The data dimensionality is estimated by using the partitioned noise-adjusted principlal component analysis (PNAPCA). The initial matrix used to start the NMF is obtained by using the fuzzy c-mean (FCM). This algorithm is capable to produce a region- or part-based representation of objects in images. Both simulated and real sensing data are used to test the algorithm.
author2	Cheng-yuan Liou
author_facet	Cheng-yuan Liou Kuang-De, Ou Yang 歐陽廣德
author	Kuang-De, Ou Yang 歐陽廣德
spellingShingle	Kuang-De, Ou Yang 歐陽廣德 Unsupervised Classification of Remote Sensing Imagery With Non-negative Matrix Factorization
author_sort	Kuang-De, Ou Yang
title	Unsupervised Classification of Remote Sensing Imagery With Non-negative Matrix Factorization
title_short	Unsupervised Classification of Remote Sensing Imagery With Non-negative Matrix Factorization
title_full	Unsupervised Classification of Remote Sensing Imagery With Non-negative Matrix Factorization
title_fullStr	Unsupervised Classification of Remote Sensing Imagery With Non-negative Matrix Factorization
title_full_unstemmed	Unsupervised Classification of Remote Sensing Imagery With Non-negative Matrix Factorization
title_sort	unsupervised classification of remote sensing imagery with non-negative matrix factorization
publishDate	2005
url	http://ndltd.ncl.edu.tw/handle/14790420395701310054
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Unsupervised Classification of Remote Sensing Imagery With Non-negative Matrix Factorization

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