請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/38664
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 劉長遠(Cheng-yuan Liou) | |
dc.contributor.author | "Kuang-De, Ou Yang" | en |
dc.contributor.author | 歐陽廣德 | zh_TW |
dc.date.accessioned | 2021-06-13T16:41:07Z | - |
dc.date.available | 2005-08-01 | |
dc.date.copyright | 2005-08-01 | |
dc.date.issued | 2005 | |
dc.date.submitted | 2005-07-03 | |
dc.identifier.citation | [1] X. Xie and G. Beni. A Validity for Fuzzy Clustering. IEEE Transactions on Pattern
Analysis and Machine Learning, Vol 13, No. 8, August 1991, 841-847. [2] A. J. Bell and T. T. Sejnowski An Information maximization approach to blind separation and blind deconvolution. Neural Comput, 7, 1129-1159 (1995). [3] Department of Information and Sciences, Nara Women’s University. Available:http://www.ics.nara-wu.ac.jp/lab/landgroup [4] J. C. Harsanyi and C.-I Chang. Hyperspectral image classification and dimensionality reduction: An orthogonal subspace projection. IEEE Transactions on Geoscience and Remote Sensing, 32 (1994), 779-875. [5] Hsuan Ren and Chein-I Chang. Automatic Spectral Target Recognition in Hyperspectral Imagery. IEEE Transactions on Aerospace and Electronic Systems, 39 (2003), 1232-1249. [6] California Institute of Technology. AVIRIS (Airborne Visible/Infrared Imaging Spectrometer)homepage. [Online]. Available:http://aviris.jpl.nasa.gov. [7] California Institute of Technology. AVIRIS (Airborne Visible/ Infrared Imaging Spectrometer)homepage. [Online]. Available: http://aviris.jpl.nasa.gov/html/aviris.spectroscopy.html [8] California Institute of Technology. AVIRIS (Airborne Visible/ Infrared Imaging Spectrometer)homepage. [Online]. http://aviris.jpl.nasa.gov/html/aviris.freedata.html [9] I.S. Dhillon and D.S. Modha. Concept decompositions for large sparse text data using clustering. Machine Learning, 42(1):143-175,2001. [10] J. B. Lee, A. S.Woodyatt, and M. Berman. Enhancement of hig spectral resolution remote sensing data by a noise-adjusted principal components transform. IEEE Trans. Geos. Remote Sensing, 28 (1990),295-304. [11] A. A. Green, M. Berman. P. Switzer, and M. Craig (1988)A transformation for ordering multispectral data in terms of image quality with implications for noise removal. IEEE Trans. Geos. Remote Sensing, 26(1), 65-74. [12] D. Lee and H. Seung. Learning the parts of objects by non-negative matrix factorization. Nature, 401:788-791, 1999. [13] D. Lee and H. Seung. Algorithms for non-negative matrix factorization. NIPS, 2000. [14] Spatial Information Clearinghouse. Available at http://maic.jmu.edu/sic/rs/ems.htm. [15] S.M. Wild, Seeding Non-Negative Matrix Factorizations with the SphericalKMeans Clustering, Thesis for the Department of Applied Mathematics, University of Colorado (April 2003). [16] T. M. Tu, H. C. SHYU., Y. S. SUN. and C. H. LEE. Determination of data dimensionality in hyperspectral imagery-PNAPCA. Multidimensional Systems and Signal Processing, 10,255-274 (1999). | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/38664 | - |
dc.description.abstract | 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. | en |
dc.description.provenance | Made available in DSpace on 2021-06-13T16:41:07Z (GMT). No. of bitstreams: 1 ntu-94-P91922001-1.pdf: 821816 bytes, checksum: 6ca965aa50194931e0f953e228301ad9 (MD5) Previous issue date: 2005 | en |
dc.description.tableofcontents | Table of Contents v
List of Tables vi List of Figures vii Abstract ix Acknowledgements x 1 Introduction 1 2 Background and Theory 3 2.1 Spectral Image Concept . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2.2 Unsupervised Classification . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.2.1 Vector Model for Multi/Hyper Spectral Images . . . . . . . . . . . 6 2.2.2 Estimate the Number of Endmembers . . . . . . . . . . . . . . . . 7 2.2.2.1 Principle Component Analysis . . . . . . . . . . . . . . 8 2.2.2.2 NAPCA . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.2.2.3 PNAPCA . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.2.3 Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.2.3.1 Distance Measure . . . . . . . . . . . . . . . . . . . . . 12 2.2.3.2 K-Means . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.2.3.3 Fuzzy C-Means . . . . . . . . . . . . . . . . . . . . . . 15 2.2.4 Non-Negative Matrix Factorization . . . . . . . . . . . . . . . . . 17 3 Experimental Analysis 19 3.1 Evaluation with Simulated Data . . . . . . . . . . . . . . . . . . . . . . . 19 3.1.1 Generation of Simulated Data . . . . . . . . . . . . . . . . . . . . 19 3.1.2 Unsupervised Clustering of Simulated Data . . . . . . . . . . . . . 21 3.1.2.1 Spherical K-Means Clustering . . . . . . . . . . . . . . 22 3.1.2.2 Hyper-spectral Fuzzy C-Means Clustering, H-FCM . . . 22 3.1.2.3 Comparison of Spherical K-Means and H-FCM Clustering 23 3.1.3 Clustering Refinement With NMF . . . . . . . . . . . . . . . . . . 27 3.2 Evaluation with AVIRIS Data . . . . . . . . . . . . . . . . . . . . . . . . . 29 3.2.1 Signal Estimation on the AVIRIS Data Set . . . . . . . . . . . . . . 31 3.2.2 Unsupervised Clustering . . . . . . . . . . . . . . . . . . . . . . . 32 3.2.3 Refine with NMF . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 3.2.4 Evaluate Clustering Results With Xie-Beni Index . . . . . . . . . . 35 4 Conclusion 39 Bibliography 40 A Nowadays multi- and hyper-spectral systems 42 | |
dc.language.iso | en | |
dc.title | 應用NMF方法分析多頻譜遙測影像 | zh_TW |
dc.title | Unsupervised Classification of Remote Sensing Imagery With Non-negative Matrix Factorization | en |
dc.type | Thesis | |
dc.date.schoolyear | 93-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 顏文明(Wen-ming Yan),李肇林(Tzao-lin Lee),黃國源(Kou-Yuan Huang),王榮華 | |
dc.subject.keyword | 非監督式分類,遙測影像,非負矩陣分解, | zh_TW |
dc.subject.keyword | Unsupervised Classification,Remote Sensing,Non-negative Matrix Factorization,Fuzzy C Means,Partitioned Noise Adjusted Principle Component Analysis, | en |
dc.relation.page | 43 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2005-07-04 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
顯示於系所單位: | 資訊工程學系 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-94-1.pdf 目前未授權公開取用 | 802.55 kB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。