非線性像元分解考慮多次反射應用於高光譜影像

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：6

、訪客IP：3.144.172.115

姓名

徐世珉(Shin-Min Syu) 查詢紙本館藏

畢業系所

遙測科技碩士學位學程

論文名稱

非線性像元分解考慮多次反射應用於高光譜影像
(Nonlinear Unmixing with Multiple Reflection for Hyperspectral Remote Sensing Imagery)

相關論文

★ 利用影像處理進行遙測影像的河道偵測與醫學影像的血管偵測	★ 可調式都卜勒主動雷達校正器之改良研究
★ 基於色彩校正的遙測影像變遷偵測	★ 應用階層式親和力傳播理論進行高光譜影像分類
★ 遙測影像中雲及其陰影的移除及雲高估計	★ 龜山島周圍海域熱液與地震的關係
★ 利用穿牆連續波雷達分析人體步態的微都卜勒效應	★ 新穎的混合式角反射器法於全極化合成孔徑雷達校正
★ 應用多光譜遙測影像進行線性及非線性水深反演模式之探討	★ 使用MODIS偵測地溫異常-熱異常和地震的相關性
★ 多光譜遙測影像自動偵測城市道路	★ 地球同步衛星觀測資料之雲區像素辨識
★ 結合掩星折射率與高光譜紅外線觀測之大氣溫溼度垂直剖面反演	★ 應用遙測影像之水深校正於東沙環礁海草棲地變遷
★ 基於SAR的數值高程模型的定性與定量分析	★ 應用多時期向日葵8號衛星影像進行雲像素的偵測與追蹤

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

遙測是指在衛星上或飛機上測量地表資訊而不需要接觸到目標。近年遙測科技蓬勃發展，而高光譜影像為遙測影像中重要的產品，它能同時利用許多感應器用來紀錄不同波段的電磁波能量值，它的光譜數量可以到達數百或數千個波段，因為高光譜有著很高的光譜解析率，所以可以分辨目標物的細微不同。
然而在空間解析度，一個像素中往往會包含多於一種物質，所以需要將物質萃取出來並估計物質的含量，這稱為像素分解。線性混合模型是目前最廣泛應用的，它假設光線沒有多次反射，也就是光經過物質反射後直接到達感測器，因此我們可以利用線性分解計算每種物質在此像素中的含量。不過若地表粗糙或因為地形複雜等原因，線性混合模型有所不足，所以有科學家提出了廣義雙線性模型，此模型加入的光在不同物質間的交互作用，然而此模型忽略了相同物質的交互作用，因此在此研究中。我們提出了一個新的模型叫做修正後的廣義雙線性模型，此模型建立在廣義雙線性模型上，加入了同物質間的交互作用，而此模型也包含了一些物理上的限制，其中包含了物質含量非負數和總合為1，同時限制了二次反射的參數式介於0到1之間。我們利用三組不同的高光譜影像來做測試，修正後的廣義雙線性模型除了可以得到此地區更多的資訊外，計算結果也好於另外兩種模式。

摘要(英)

Remote sensing is to measure the object properties on the earth’s surface using data acquire from aircrafts and satellites. Hyperspectral imaging spectrometers record electromagnetic energy scattered in their instantaneous field of view with hundreds or thousands of spectral channels. This high spectral resolution improves the capability for material identification via spectroscopic analysis.
However, because of spatial resolution, each pixel in hyperspectral images usually contains more than one material. Linear mixture model (LMM) is developed for this problem and has been widely studied. This model assumes that the spectrum of a pixel is linearly combined by all the resident materials with their corresponding abundance, and it ignores the reflection between materials. Nonlinear models have recently drawn lots of attentions for spectral unmixing. The generalized bilinear model (GBM) has been proposed for nonlinear mixture which considers the second order interactions between two different endmembers. However, it neglects the possibility of second order interactions between the same endmembers. In this study, we propose a modified GBM (MGBM) by considering second order reflection between all the endmembers. The non-negativity and sum-to-one constraints for the abundances are ensured by the proposed algorithms.

關鍵字(中)

★ 高光譜
★ 非線性像元分解
★ 改進的廣義雙線性模型

關鍵字(英)

★ Hyperspectral Images
★ Nonlinear Unmixing
★ Modified Generalized Bilinear Model.

論文目次

摘要 i
ABSTRACT ii
Contents iii
List of Figures v
List of Tables vii
Acronyms viii
Chapter 1 Introduction 1
1.1 Background 1
1.2 Spectral Unmixing 2
1.3 Purpose of Research 4
1.4 Flow Chart 5
1.5 Thesis Orgnization 6
Chapter 2 Literature Review 7
2.1 Linear Mixing Model (LMM) 7
2.2 Generalized Bilinear Model (GBM) 7
2.3 Fully Constrained Least Squares 8
2.4 Nonnegativity Constrained Least Square Algorithm 9
2.5 Vertex Component Analysis 12
Chapter 3 Methodology 15
3.1 Modified Generalized Bilinear Model 15
3.2 Fan-FCLS Algorithm 15
Chapter 4 Experiment Result 19
4.1 Simulation 20
4.2 Real Image Scene 21
4.2.1 AVIRIS sensor 21
4.2.2 ROSIS sensor 23
4.2.3 HYDICE sensor 24
4.3 Experiment results 26
4.3.1 Moffett Field Scene 26
4.3.2 Pavia University Scene 30
4.3.3 Washington DC Mall Scene 35
Chapter 5 Conclusions and Future works 42
5.1 Conclusions 42
5.2 Future Works 43
References 44

參考文獻

[1] Schowengerdt, R.A. (2007). “Remote Sensing, Models, and Methods for Image Processing.” 3rd ed. Burlington, MA: Academic Press.
[2] Keshava, N.; Mustard, J.F. (2002). “Spectral Unmixing.” Signal Processing Magazine, IEEE. 19(1): p. 44-57.
[3] Bioucas-Dias, J.M.; Plaza, A.; Dobigeon, N.; Parente, M.; Qian, D.; Gader, P.; Chanussot, J. (2012). “Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 5(2): p. 1939-1404.
[4] Dobigeon, N.; Tourneret, J.-Y. ; Richard, C.; Bermudez, J.C.M.; McLaughlin, S.; Hero, A.O. (2014). “Nonlinear Unmixing of Hyperspectral Images: Models and Algorithms.” Signal Processing Magazine, IEEE. 31(1): p. 82-94.
[5] Halimi, A.; Altmann, Y.; Dobigeon, N.; Tourneret, J.Y. (2011). “Nonlinear Unmixing of Hyperspectral Images Using a Generalized Bilinear Model.” Statistical Signal Processing Workshop, IEEE. p. 413-416.
[6] Halimi, A.; Altmann, Y.; Dobigeon, N.; Tourneret, J.Y. (2011). “Unmixing Hyperspectral Images Using the Generalized Bilinear Model” IEEE International on Geoscience and Remote Sensing Symposium. p. 1886-1889.
[7] Heinz, D.C.; Chang, C.I. (2001). “Fully Constrained Least Squares Linear Spectral Mixture Analysis Method for Material Quantification in Hyperspectral Imagery.” IEEE Transactions on Geoscience and Remote Sensing. 37(3): p. 529-545.
[8] Charles L.L.; Richard J.H. (1995). “Solving Least Squares Problems.” 1st ed. Philadelphia : SIAM.
[9] Chang, C.I.; Heinz, D.C. (2000). “Constrained Subpixel Target Detection for Remotely Sensed Imagery.” IEEE Transactions on Geoscience and Remote Sensing. 38(3): p. 1144-1159.
[10] Rasmus, B.; Sijmen, D. J. (1997). “A Fast Non-negativity-constrained Least Squares Algorithm.” J. Chemom. 11: p.393-401
[11] Nascimento, J.M.P. ; Bioucas Dias, J.M. (2005). “Vertex Component Analysis: A Fast Algorithm to Unmix Hyperspectral Data” Transactions on Geoscience and Remote Sensing, IEEE. 43(4): p.898-910.
[12] 張兵, 高連,. 高光譜圖像分類與目標探測. 科學出版社, 2011
[13] Fan, W.; Hu, B.; Miller, J.; Li, M. (2009). “Comparative Study Between a New Nonlinear Model and Common Linear Model for Analysing Laboratory Simulated-forest Hyperspectral Data.” International Journal of Remote Sensing. 30(11): p. 2951-2962.
[14] Jet Propulsion Laboratory California Institute of Technology, AVIRIS website, http://aviris.jpl.nasa.gov/
[15] Mitchell, P.A. (1995). “Hyperspectral digital imagery collection experiment (HYDICE).” Geographic Information Systems, Photogrammetry, and Geological/Geophysical Remote Sensing, SPIE. 2587: p70-95.
[16] Mueller, A.A.; Hausold, A.; Strobl P. (2002). “HySens-DAIS/ROSIS Imaging Spectrometers at DLR.” Remote Sensing for Environmental Monitoring, GIS Applications, and Geology, SPIE. 4545: p.225-235.

指導教授

任玄

審核日期

2014-7-30

推文