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姓名 魏子軒(Tzu-Hsuan Wei)  查詢紙本館藏   畢業系所 資訊工程學系
論文名稱 高頻譜影像物質含量估計運用加權最小 平方法
(Weighted Least Square Methods for MaterialAbundance Estimation in Hyperspectral Image )
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摘要(中) 近年來,高頻譜影像已普遍應用於遙測影像之目標物偵測,其優點在於接近連續的數百個頻譜波段可以提供較多高頻譜解析度以解決多頻譜影像無法辨認出之物質。然而在高頻譜影像中感興趣之目標物尺寸一般皆小於地面解析度,在這個情況下,便必須使用次像素目標偵測法。
線性頻譜混合分析是一個常用於高頻譜影像中次像素目標物偵測與物質分類之技術,而最小平方誤差方法則為一個普遍用於解決物質含量估計之線性頻譜混合問題。在本篇論文中將介紹一個一般化之最小平方法,加權最小平方法。當使用不同的加權矩陣時,即可推導出不同的偵測或分類演算法。我們將證明過去一些已發表之方法,皆可重寫為加權最小平方法的形式。為了產生更準確之物質含量,我們接著結合含量總合為1與含量不為負的兩個限制條件,成為完全限制加權最小平方法。而為了更進一步應用所設計之演算法在沒有任何物質資訊的影像中,我們亦加入一個以最小平方誤差為基礎之非監督式方法,將完全限制加權最小平方法延伸為一非監督式演算法。最後,我們比較幾個高頻譜影像雜訊估計之方法,以提高本方法之偵測效能。
在本論文之電腦模擬與真實高頻譜影像實驗中,我們發現經過白化雜訊處理後之最小平方法偵測結果較好,另外亦可顯現出完全限制加權最小平方法在物質含量估計之效能也較好。
摘要(英) Recently, hyperspectral images are widely used for target detection in remotely sensed imagery. They take advantage of hundreds of contiguous spectral channels to uncover materials that usually cannot be resolved by multispectal images. However, the ground resolution in hyperspectral imagery is generally larger than the size of targets of interest, under this circumstance target detection must be carried out at subpixel level.
Linear spectral mixture analysis (LSMA) is a widely used technique for subpixel target detection and material classification in hyperspectral image, and least squares unmixing methods are widely used to solve linear mixture problems for material abundance estimation. In this thesis, a weighted least squares (WLS) method is introduced as a generalization. When different weight matrix is applied, a certain detector or classifier will be resulted. Several previous proposed methods have been proven to be versions of WLS methods. For accurate abundance fraction estimation, a fully constrained weighted least squares (FCWLS) approach is developed by combining sum-to-one and nonnegativity constraints. In order to further apply the designed algorithm to unknown image scenes, an unsupervised least squares method is also proposed. Furthermore, several noise estimation methods are introduced, and we also compare the performance of target detection capability.
A serious of computer simulation and real hyperspectral image experiments were conducted in this thesis. The experimental results showed that the noise whitening least squares method in target detection and FCWLS approach in abundance fraction estimation have better performance.
關鍵字(中) ★ 物質含量估計
★ 次像素目標物偵測
★ 線性頻譜混合分析
★ 高頻譜影像
★ 最小平方法
關鍵字(英) ★ material abundance estimation
★ subpixel target detection
★ hyperspectral image
★ least squares method
★ linear spectral mixture analysis
論文目次 Abstract i
摘 要 ii
目 錄 v
附圖目錄 vii
附表目錄 viii
第 一 章 緒 論 1
1.1 研究動機與目標 1
1.2 相關研究 3
1.3 論文架構 7
第 二 章 線 性 頻 譜 混 合 分 析 8
2.1 線性頻譜混合模型 8
2.2 最小平方估計(Least Squares Estimate) 10
2.3 部分限制最小平方法(Partially Constrained Least Squares) 10
2.4 實作NCLS演算法 14
2.5 完全限制最小平方法(Fully Constrained Least Squares, FCLS) 16
第 三 章 加 權 最 小 平 方 法 18
3.1加權最小平方法(Weighted Least Squares Approach) 18
3.2 加權最小平方法之比較 19
3.3 部分限制加權最小平方法(Partially Constrained WLS) 23
3.4 完全限制加權最小平方法(Fully Constrained WLS, FCWLS) 26
第 四 章 非 監 督 式 演 算 法 27
4.1 UFCLS演算法原理 28
4.2 實作UFCLS演算法 29
4.3 UFCLS演算法範例說明 30
第 五 章 估 計 雜 訊 協 方 差 矩 陣 34
5.1 以空間濾波器(Spatial Filter)為基礎 35
5.2 以訊號正交子空間(Signal Orthogonal Subspace Projection)為基礎 36
5.3 以主成分分析(Principal Component Analysis, PCA)為基礎 36
5.4 以線性預測(Linear Prediction)為基礎 38
第 六 章 實 驗 結 果 41
6.1 實驗環境 41
6.2 電腦模擬實驗設計 43
6.3 雜訊估計之效能評估實驗 45
6.3.1 電腦模擬實驗 45
6.3.2 AVIRIS高頻譜影像實驗 47
6.4 監督式目標物偵測與物質量化實驗 49
6.4.1 電腦模擬實驗 49
6.4.2 AVIRIS高頻譜影像實驗 51
6.5 非監督式目標物偵測與物質量化實驗 54
6.5.1 AVIRIS高頻譜影像實驗 55
6.5.2 Hyperion高頻譜影像實驗 57
第 七 章 結論與未來研究方向 64
7.1 結論 64
7.2 未來研究方向 66
參考文獻 68
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指導教授 范國清、任玄
(Kuo-Chin Fan、Hsuan Ren)
審核日期 2005-7-15
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