無參數加權特徵萃取對遙測及醫學影像目標偵測的應用

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：8

、訪客IP：18.191.102.112

姓名

紀萬偉(Wan-wei Chi) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

無參數加權特徵萃取對遙測及醫學影像目標偵測的應用
(Nonparametric Weighted Feature Extraction for Target Detection in Remote Sensing and Medical Images)

相關論文

★ 基於GPU的SAR資料庫模擬器：SAR回波訊號與影像資料庫平行化架構 (PASSED)	★ 高頻譜影像物質含量估計運用加權最小平方法
★ 利用X光乳房攝影產生之紋理特徵影像在腫瘤偵測上之研究	★ 高光譜影像雜訊模式估計
★ 利用高光譜影像作異常物偵測	★ 利用電腦自動化對數值高程模型作線形偵測
★ 高光譜影像異常物偵測與識別之平行運算方法與其效能評估	★ 利用多光譜影像的光譜與空間資訊結合數學型態學進行海洋油汙偵測
★ 低解析度車牌視訊之強化與辨識	★ 利用遙測影像自動萃取校正點
★ 新的影像融合演算法應用於多光譜遙測影像	★ 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
★ 利用固定式攝影機即時偵測土石流	★ 藉由電腦視覺自動偵測土石流

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)

線性混合模式(Linear spectral mixture analysis)已經廣泛的被應用在遙測領域上，而最小平方誤差(Least Squares)是眾多有效處理線性混合模式的方法之一。雜訊在線性混合模式中的每一各波段不一定是呈現獨立且均勻分佈(Independent and Identical Distributed，(i.i.d))，而雜訊白化最小平方誤差(Noise Whitening Least Squares，(NWFE))已經被推導證明能改善傳統最小平方誤差法的效能藉由雜訊白化處理把雜訊分佈改成i.i.d.。然而如何去估計出雜訊的共變異數矩陣仍然是一個重要的問題。已經有許多方法被提出來估計雜訊的分佈，包含空間的高通濾波器、頻率域的高通濾波器、正交子空間投影、主成份分析法和費雪線性區別法(Fisher’s Linear Discriminant Analysis，(Fisher’s LDA))。這些方法在雜訊是高斯分佈時都有很好的估計，但是當雜訊的分佈不是高斯分佈時則不理想。這篇文章中我們採用無參數加權特徵萃取(Nonparametric Weighted Feature Extraction，(NWFE))來估計雜訊的分佈並和過去的一些方法做比較。同時限制能量最小化法(Constrained Energy Minimization，(CEM))在遙測的目標物偵測上我們也加上權重改善CEM對目標光譜過於敏感的問題。最後我們並應用於MRI醫學影像，透過遙測的演算法對MRI影像做個分類與效能比較。

摘要(英)

Linear spectral mixture analysis (LSMA) has been widely used in remote sensing applications, and the Least Squares (LS) approach is one of the most effective methods for solving LSMA problem. Since the noise in LSMA from each band may not be independent and identical distributed (i.i.d.), it has been proven mathematically that the Noise Whitening Least Squares (NWLS) will outperform the original LS by making the noise i.i.d. with the noise whitening process. But how to estimate the noise covariance matrix is remain a challenge problem. Many methods have been proposed in the past which including spatial high-pass filter, frequency domain high-pass filter, orthogonal subspace projection, principal component analysis and Fisher’s Linear Discriminant Analysis (Fisher’s LDA) based approach. They all perform well for Gaussian noise but encounter problems when the noise is ill distributed. In this study, we adopt Nonparametric Weighted Feature Extraction (NWFE) to estimate the noise distribution and compare the results. Furthermore, we also apply the weighted factor for Constrained Energy Minimization (CEM) to reduce its object spectrum sensitivity problem. Finally, we apply these methods to MRI medical images and discuss the results.

關鍵字(中)

★ 無參數加權特徵萃取
★ 最小平方誤差法
★ 限制能量最小化法
★ 線性混合模式
★ 雜訊白化最小平方誤差法

關鍵字(英)

★ Linear Spectral Mixture Analysis
★ Least Squares
★ Noise Whitening Least Squares
★ Nonparametric Weighted Feature Extraction
★ Constrained Energy Minimization

論文目次

摘要............................................i
Abstract.......................................iii
目錄...........................................iv
圖目錄........................................vi
表格目錄.....................................ix
第一章緒論......................................1
1.1、研究動機與方法..............................1
1.2、影像介紹....................................2
1.2.1、遙測影像..................................2
1.2.2、醫學影像..................................5
1.3、論文架構....................................6
第二章目標偵測法回顧............................8
2.1、線性光譜混合模型............................8
2.2、最小平方誤差法..............................9
2.3、完全限制最小平方法.........................10
2.4、雜訊白化最小平方估計法.....................12
2.5、費雪線性區分法.............................14
2.6、無參數加權特徵萃取.........................17
2.7、限制能量最小化法...........................20
2.8、維度的拓展.................................22
2.9、形態學.....................................24
第三章無參數加權應用於NWLS與CEM的改進..........26
3.1、使用NWFE來估計雜訊.........................26
3.2、LS為基礎的實驗.............................28
3.2.1、遙測多光譜影像...........................29
3.2.2、醫學影像.................................37
3.3、加權抑制能量最小化法.......................46
3.4、CEM與WCEM實驗 .............................48
3.4.1、高光譜影像...............................48
3.4.2、SPOT-5衛星影像...........................53
3.4.3、MRI醫學影像 ..............................57
第四章結論與未來研究方向.......................62
參考資料........................................64

參考文獻

[1] C. L. Lawson and R. J. Hanson, “Solving least squares problems”, in Proc. Classics in Applied Mathematics, Philadelphia, PA, 1995.
[2] D. Heinz and C.-I Chang, “Fully constrained least squares linear mixture analysis for material quantification in hyperspectral imagery”, IEEE. Geosciences, Remote Sensing., vol.39, no. 3, pp. 529-545, Mar. 2001.
[3] B. Bro and S. D. Jong, “A fast non-negativity constrained least squares algorithm”, J. Chemom. , vol.11, pp.393-401, 1997.
[4] H. Ren, Q. Du and J. Jeasen, “Constrained Weight Least Squares Approaches for target detection and classification in Hyperspectral Imagery”, IGARSS 02, pp.3426-3428, 2002.
[5] C.-I Chang, Hyperspectral Imaging : Techniques for spectral Detection and classification, New York : Kluwer, 2003.
[6] B.-C. Kuo and D. A. Landgrebe, “Nonparametric Weighted Feature Extraction for Classification”, IEEE Transactions on Geosciences and Remote sensing, vol.42, no.5, pp. 1096-1105, May 2004.
[7] H. Ren and C.-I Chang, “A generalized orthogonal subspace projection approach to unsupervised multispectral image classification”, IEEE Transactions on Geosciences and Remote sensing, vol.38, no.3, pp. 2512-2528, 2000.
[8] S. Shen, W. A. Sandham and M. H. Granat, “Preprocessing and segmentation of magnetic resonance imaging”, in Proc. 4th IEEE Annu. Int. Conf. Inform. Technol. Appl. Biomed., ITAB’03, U.K., pp.149-152, 2003.
[9] C.-W. Chen, “The Estimation of Noise Covariance Matrix in Hyperspectral Remotely Sensed Images”, Proc. Of SPIE, Image Spectrometry XI, vol.39, 63020D1-9, 2006.
[10] C.-I Chang and B. Ji, “Weighted Abundance linear spectral mixture analysis”, IEEE Transactions on Geosciences and Remote sensing, vol.44, no.2, February 2006.
[11] C.-M. Wang, S.-C. Yang, P.-C. Chung, C.-C. Chen, C.-W. Yang and C.-I Chang, “Detection of Spectral Signatures in Multispectral MR Images for Classification”, IEEE Transactions on Medical Imaging, vol.22, no.1, pp. 50-61, Jan. 2003.
[12] C.-M. Wang, S.-C. Yang, P.-C. Chung, C.-I Chang and C.-C. Chen, “Orthogonal Subspace Projection-Base Approaches to Classification of MR Images Sequences”, Computerized Medical Imaging and Graphics, vol.25, no.6, pp. 465-467, Dec. 2001.
[13] R.A. Fisher, “The Statistical Utilization of Multiple Measurements”, Ann. Eugenics, vol. 8 pp. 376-386, 1938.
[14] R.O. Duda and P.E. Hart, Pattern Classification and Scene Analysis, New York: John Wiley & Sons, 1973.
[15] C.-M. Wang, C.-C. Chen, S.-C. Yang, P.-C. Chung, Y.-N. Chung, C.-W. Yang and C.-I Chang “Unsupervised Orthogonal Subspace Projection Approach to MR Image Classification”, Optical Engineering, vol.41, no.7, pp. 1546-1557, July 2002.
[16] AVIRIS Web-Page, http://aviris.jp1.nasn.gov

指導教授

任玄(Hsuan Ren)

審核日期

2007-7-18

推文