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。

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.

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