請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/82208完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 蔡政安(Chen-An Tsai) | |
| dc.contributor.author | Lo-Chi Wang | en |
| dc.contributor.author | 王洛騏 | zh_TW |
| dc.date.accessioned | 2022-11-25T06:33:41Z | - |
| dc.date.copyright | 2021-08-18 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-06-30 | |
| dc.identifier.citation | Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32. Breiman, L., Cutler, A. (2001). Random Forests Leo Breiman and Adele Cutler. Berkeley Statistics. https://www.stat.berkeley.edu/~breiman/RandomForests/ Cortes, C., Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3), 273-297. Fukunaga, K., Koontz, W. L. (1970). Representation of random processes using the finite Karhunen-Loeve expansion. Information and Control, 16(1), 85-101. Jolliffe, I. T. (2003). Principal component analysis. Technometrics, 45(3), 276. Lehnert, L. W., Meyer, H., Obermeier, W. A., Silva, B., Regeling, B., Bendix, J. (2019). Hyperspectral Data Analysis in R: The hsdar Package. Journal of Statistical Software, 89(1), 1-23. Li, H., Xiao, G., Xia, T., Tang, Y. Y., Li, L. (2013). Hyperspectral image classification using functional data analysis. IEEE transactions on Cybernetics, 44(9), 1544-1555. Mateen, M., Wen, J., Nasrullah, Akbar, M. A. (2018). The role of hyperspectral imaging: a literature review. International Journal of Advanced Computer Science and Applications, 9(8), 51-62. Nie, Y., Wang, L., Liu, B., Cao, J. (2018). Supervised functional principal component analysis. Statistics and Computing, 28(3), 713-723. Ramsay, J., Silverman, B. W. (2006). Functional Data Analysis. Springer Science Business Media. Ramsay, J. O., Hooker, G., Graves, S. (2009). Functional data analysis with R and MATLAB. Springer Science Business Media. Wang, Q., Meng, Z., Li, X. (2017). Locality adaptive discriminant analysis for spectral–spatial classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters, 14(11), 2077-2081. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/82208 | - |
| dc.description.abstract | "不同於每個像素由紅、綠、藍組成的三原色光模式 (RGB color model)，高光譜影像 (hyperspectral imaging, HSI) 在連續波長範圍內提供了更詳細的光譜資訊。而在分析HSI主要面臨的挑戰為其紀錄的光譜具有無限維的特徵空間，以及相對有限的樣本。直觀而言，可將HSI在每個像素的資料視為波長的函數，而函數主成分分析 (functional principal component analysis, FPCA) 能夠對此類型資料進行維度縮減。FPCA是主成分分析 (principal component analysis, PCA) 的延伸，即一種針對函數型資料的降維方法。從傳統的FPCA估計出的函數主成分 (functional principal components, FPCs) 是由可解釋多少函數資料的變異來進行排序，並無將反應變數納入考量。在本研究中，提出了兩種監督式判別方法 (支援向量機和隨機森林) 來對這些FPCs重新排名。而在降維後，便可利用機器學習演算法進行後續的統計分析。我們將透過三個實際資料應用、兩筆模擬資料，來評估提出的方法之可行性。" | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-25T06:33:41Z (GMT). No. of bitstreams: 1 U0001-2806202111421000.pdf: 1805039 bytes, checksum: f5ab2a9fdf442895aa754dec877137c0 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 摘要 i Abstract ii List of Figures vi List of Tables viii 1. Introduction 1 1.1. Hyperspectral Imaging 1 1.2. Functional Data Analysis 2 2. Methodology 4 2.1. Computational Details 6 2.1.1. Transform HSI to Functional Data 6 2.1.2. Functional Principal Component Analysis 7 2.2. Supervised Functional Principal Component Analysis 8 2.2.1. Continuous Response Variable 9 2.2.2. Binary Response Variable 9 2.3. Method I 10 2.3.1. Continuous Response Variable 10 2.3.2. Categorical Response Variable 11 2.4. Method II 11 2.4.1. Continuous Response Variable 11 2.4.2. Categorical Response Variable 12 3. Applications 13 3.1. Case I: Chlorophyll Content 13 3.2. Case II: Detection of Cancer 17 3.3. Case III: Indian Pines Image 21 4. Simulation Studies 27 4.1. The First Simulation Study 27 4.2. The Second Simulation Study 35 5. Conclusion 41 References 43 | |
| dc.language.iso | en | |
| dc.subject | 函數主成分分析 | zh_TW |
| dc.subject | 高光譜影像 | zh_TW |
| dc.subject | 隨機森林 | zh_TW |
| dc.subject | 支援向量機 | zh_TW |
| dc.subject | 函數資料分析 | zh_TW |
| dc.subject | random forests | en |
| dc.subject | hyperspectral imaging | en |
| dc.subject | functional data analysis | en |
| dc.subject | functional principal component analysis | en |
| dc.subject | support-vector machines | en |
| dc.title | 函數資料分析在高光譜影像資料之研究 | zh_TW |
| dc.title | Application of Functional Data Analysis to Hyperspectral Imaging | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 蔡欣甫(Hsin-Tsai Liu),邱春火(Chih-Yang Tseng) | |
| dc.subject.keyword | 高光譜影像,函數資料分析,函數主成分分析,支援向量機,隨機森林, | zh_TW |
| dc.subject.keyword | hyperspectral imaging,functional data analysis,functional principal component analysis,support-vector machines,random forests, | en |
| dc.relation.page | 44 | |
| dc.identifier.doi | 10.6342/NTU202101165 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2021-06-30 | |
| dc.contributor.author-college | 共同教育中心 | zh_TW |
| dc.contributor.author-dept | 統計碩士學位學程 | zh_TW |
| dc.date.embargo-lift | 2023-12-01 | - |
| 顯示於系所單位： | 統計碩士學位學程 | |
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