影像融合技術應用於地表分類之探討

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：12

、訪客IP：18.191.13.255

姓名

吳孟哲(Meng-Che Wu) 查詢紙本館藏

畢業系所

太空科學研究所

論文名稱

影像融合技術應用於地表分類之探討
(A study of image fusion applied in land cover classification)

相關論文

★ 2.4GHz無線傳輸系統於遙測與GPS數據整合之研製	★ 2.4GHz之無線電波室內傳播通道特性量測與分析
★ K波段地面鏈路降雨衰減效應之研究	★ 多層非均勻介質之微波散射模擬分析
★ Ka 波段地面鏈路降雨效應與植被遮蔽效應之研究	★ 地面遙測影像雷達發射與接收模組之設計
★ 合成孔徑雷達之移動目標物速度估測研究	★ 小波轉換於合成孔徑雷達干涉相位雜訊之研究
★ Ka波段台灣地區降雨及地面環境傳播特性研究	★ 雨滴粒徑分佈應用於Ka波段降雨衰減估計之研究
★ 全偏極合成孔徑雷達非監督式目標分類與極化方位角偏移效應估算之研究	★ 全偏極合成孔徑雷達於目標分類之研究
★ 應用共軛梯度演算法在掃描式合成孔徑雷達目標物特徵增強處理	★ 台灣北部地區Ka波段降雨衰減模式之研究
★ 雨滴粒徑與植被遮蔽效應對Ka波段電波衰減影響之探討	★ 基因演繹法於全偏極合成孔徑雷達影像對比強化最佳化之研究

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)

本論文嘗試討論兩個主題：主題一為利用主成份分
析PCA方法應用於像元階層資料融合技術的研究。主題二為應用
Dempster-Shafer evidence theory方法於特徵階層資料融合技術的研
究。
在第一個主題中，由於合成孔徑雷達的資料具有全偏極特性，在此
選取了對植被較為敏感的HV極化合成孔徑雷達資料，與具有光譜特
性的光學SPOT資料做資料融合處理以利接下來的地物分類。首先，
本研究利用小波轉換技術來濾除合成孔徑雷達斑駁雜訊，在接下來融
合步驟中，主成分分析出來的第一部分(PC1)是用做完濾除雜訊後的
合成孔徑雷達取代，在資料融合後，進行地物分類是採用最大似然法
來分類融合影像。
在第二個主題中，利用全偏極雷達資料的極化特性結合SPOT資料
的光譜特性，其主要目的是為了增加分類的精確度。首先使用李式濾
波器濾除全偏極雷達資料雜訊，接下來同樣是使用採用最大似然法來
分類融合影像，(不同的在於全偏極雷達影像使用Wishart機率分布，
在光學影像採用multivariate Gaussian 機率分布) 將每個類別中每個
像元屬於某個類別的機率值計算出來，再利用 Dempster-Shafer
evidence theory 來結合這些類別的機率值。最後產生出一張新的分
類影像。
實驗的結果顯示分類的精確度比較於未融合的資料都有明顯提升
的效果，也證明了此兩個資料融合方法對於不同資料特性的融合都是
很成功的。

摘要(英)

There are two main topics will discuss in this paper, pixel-level image
fusion based on Principle Component Analysis (PCA), and feature-level
image fusion based on Dempster-Shafer evidence theory.
In pixel-level case, the SAR image at HV polarization is relatively
sensitive to the vegetation canopy. We combined the HV polarization
information from SAR and spectral characteristic from SPOT images in
an effort to enhance land cover classification. Before the fusion process,
wavelet transform was first applied to denoise the SAR image which
suffers from speckle contamination due to coherent process. The principle
component analysis (PCA) is used to fuse the SPOT and SAR images. In
so doing, the PC-1 component is replaced by SAR image (approximation
image, after wavelet transform) and then the inverse transform is
followed. At last, the maximum likelihood classifier was used for both
SPOT-XS images and fusion images.
In feature-level case, fully polarization information from SAR is used
to combine with spectral characteristic from SPOT images, mainly to
enhance land cover classification as well. We first denoise the SAR image
by Lee filter. Next, the maximum likelihood classifier based on different
distribution was used for SAR and SPOT images ( Based on Wishart
distribution and multivariate Gaussian distribution respectively), to
extract the conditional probability of each pixel for each class.
Dempster-Shafer evidence theory is then applied, to combine the
classified results of SAR and SPOT data.
Experimental results show that the classification accuracy is
dramatically improved by making use of the proposed methods above.
Data fusion can take advantage of the use of complementary information
to obtain a better overall accuracy than using single data source only.

關鍵字(中)

★ 影像融合

關鍵字(英)

★ image fusion

論文目次

Chapter 1 Introduction 1
1.1 Image fusion 1
1.2 Objective and motivation 3
Chapter 2 Polarimetric Synthetic Aperture Radar 5
2.1 Polarimetric SAR 5
2.2 Polarimetric description of scatterers 12
2.2.1 Scattering matrix 13
2.2.2 Covariance matrix 14
Chapter 3 Pixel-level image fusion 16
3.1 Wavelet Filter 16
3.2 Fusion method based on principle component analysis 19
3.2.1 Principle component analysis (PCA) 19
3.2.2 Image fusion based on Principle Component Analysis (PCA) 22
3.2.3 Supervised classification based on the Multivariate Gaussian probability density distribution 23
3.3 Experimental test result 28
3.3.1 Test data sets description 28
3.3.2 Classification result and discussion 30
Chapter 4 Feature-level image fusion 45
4.1 Dempster-Shafer evidence theory 45
4.2 Maximum likelihood classifier based on the complex Wishart distribution 54
4.3 Experimental test result 55
4.3.1 Test data sets description 55
4.3.2 Supervised classification case 59
4.3.3 Unsupervised classification case 70
Chapter 5 Conclusion 80
Reference …………………………………………………………...………………..82

參考文獻

[1] CHENG, P., Toutin, T., and POHL, C., 1995, A comparison of
geometric models for multisource data fusion. Remote Sensing, GIS
and GPS in Sustainable Development and Environmental Monitoring,
Geoinformatics’ 95, Proceedings of International Symposium, 26-28
May 1995, Hong Kong (Hong Kong: Chinese University of Hong
Kong), pp. 11-17.
[2] GENDEREN, J. L. VAN, and POHL, C., 1994, Image fusion: Issues,
techniques and applications. Intelligent Image Fusion, Proceedings
EARSeL Workshop, Strasbourg, France, 11 September 1994, edited by
J. L. van Genderen and V. Cappellini (Enschede: ITC), pp.18-26.
[3] HALL, D. L., 1992, Mathematical techniques in multisensor data
Fusion (Norwood: Artech House Inc.).
[4] KEYS, L. D., SCHMIDT, N. J., and PHILLIPS, B. E., 1990, A
prototype example of sensor fusion used for a siting analysis.
Technical Papers 1990, ACSM-ASPRS Annual Convention, Image
Processing and Remote Sensing, 4, 238-249.
[5] ROGERS, R. H., and WOOD, L., 1990, The history and status of
merging multiple sensor data: an overview. Technical Papers 1990,
ACSM-ASPRS Annual Convention, Image Processing and Remote
Sensing, 4, 352-360.
[6] SHEN, S. S., 1990, Summary of types of data fusion methods utilized
in workshop papers. Multisource Data Integration in Remote Sensing,
Proceedings of Workshop, Maryland, U.S.A., 14-15June 1990, NASA
Conference Publication 3099 (Greenbelt, MD: NASA), pp.145-149.
[7] TOUTIN, T., 1994, Multisource data integration with an integrated
and unified geometric modeling. Proceedings of 14th EARSeL
Symposium on Sensors and Environmental Applications, 6-8 June
1994, Goteborg, Sweden (Paris: European Space Agency), pp.163-174
[8] Ulaby, F. T., C. Elachi, Radar Polarimetry for Geoscience
Applications, Artech House, 1990.
[9] J.S. Lee M.R. Grunes, T.L. Ainsworth, L. Du, D.L. Schuler, S.R.
Cloude, < ="" by="" applying="" target="" decomposition="" and="" complex="" wishart="" distribution
="">> PIERS 1998, Nantes, France, 13-17 July 1998, also IEEE TGRS,
vol. 37, no.5, pp2249-2258, Sept. 1999.
[10] E. Rignot, R. Chellappa, and P. Dubois, “Unsupervised segmentation
of polarimetric SAR data using the covariance matrix,” IEEE Trans.
Geosci. Remote Sensing, vol.30, pp.697-705, July 1992.
[11] J. J. van Zyl, “Unsupervised classification of scattering mechanisms
using radar polarimetry data,” IEEE Trans. Geosci. Remote Sensing,
vol. 27, pp.36-45, Jan. 1989.
[12] S. R. Cloude and E. Pottier, “An entropy based classification scheme
for land applications of polarimetric SAR,” IEEE Trans. Geosci.
Remote Sensing, vol.35, pp.68-78, Jan. 1997.
[13] J. S. Lee et al., “Intensity and phase statistics of multilook
polarimetric and interferometric imagery,” IEEE Trans. Geosci.
Remote Sensing, vol.32, Sept. 1994.
[14] S. R. Cloude and E. Pottier, “A review of target decomposition
theorems in radar polarimetry,” IEEE Trans. Geosci. Remote Sensing,vol. 34, pp.498-518, Mar. 1996
[15] T. Lee, J. A. Richards, and P. H. Swain, “Probabilistic and evidential
approaches for multisource data analysis,” IEEE Trans. Geosci.
Remote Sensing, vol. GRS-25, pp. 283-293, May 1987.
[16] A. H. Schistad Solberg, A. K. Jain, and T. Taxt, “Multisource
classification of remotely sensed data: Fusion of Landsat TM and
SAR images,” IEEE Trans. Geosci. Remote Sensing, vol.32,
pp.768-778, July 1994.
[17] Misiti, M., 2002. Wavelet toolbox for use with matlab. Wavelet
toolbox user’s guide by the MathWorks Inc. http://www.mathworks.com/access/helpdesk/help/pdf_doc/wavlet/wavelet_ug.pdf. (accessed 04/15/2004)
[18] Pohl, C., van Genderen, J. L., 1998. Multisensor image fusion in
remote sensing: concepts, methods and applications. Int. J. Remote
Sensing, vol.19, No.5, pp. 823-854.
[19] Zhou, J., Civco, D.L. and Silander, J.A, 1998, A wavelet transform
method to merge Landsat TM and SPOT panchromatic data. Int. J.
Remote Sensing, vol.19, No.4, pp.743-757.
[20] Carper, W.J., Lilesand, T.W., Kie.er, R.W., 1990. The use of
Intensity-Hue-Saturation transformation for merging SPOT
panchromatic and multispectral image data, RE&RS 56 (4) 459-467.
[21] Chavez, P.S., Sides, S.C., Anderson, J.A., 1991. Comparison of three
different methods to merge multiresolution and multispectral data:
Landsat TM and SPOT panchromatic, PE&RS(57) 295-303.
[22] D. Dubois, H. Prade, Representation and combination of uncertainty
with belief functions and plausibility measures, Comput. Intell. 4
(1998) 244-264.
[23] S. Le Hégarat-Mascle, I. Bloch, D. Vidal-Madjar, Application of
Dempster-Shafer evidence theory to unsupervised classification in
multisource remote sensing, IEEE Trans. Geosci. Remote Sensing
35 (4) (1997)
[24] 陳家堂, “全偏極合成孔徑雷達於目標分類之研究,” 國立中
央大學太空科學研究所博士論文, 2002.

指導教授

陳錕山(K.S. Chen)

審核日期

2005-7-4

推文