線性複合模式應用於變遷偵測之研究

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姓名

孫彬修(Pin-Hsiu Sun) 查詢紙本館藏

畢業系所

土木工程學系

論文名稱

線性複合模式應用於變遷偵測之研究
(Application of Linear Mixing Model for Change Detection)

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摘要(中)

隨著衛星影像的持續接收，利用衛星影像進行土地變遷偵測更趨頻繁，為使變遷偵測朝向高精度及高效率，變遷偵測的方法不斷的提出。本論文使用多時期分類法進行變遷偵測，以線性複合模式作為分類器，最小二乘子空間投影法作為求解方式，產生變遷類別影像，稱為單層次線性複合模式變遷偵測法。但由於單層次線性複合模式具有變遷組合類別數必須小於合併影像波段數限制，因此本論文進一步以多層次(Multi-Level)線性複合模式進行變遷偵測。本論文測試3組影像，使用多層次線性複合模式進行變遷偵測，其模擬影像變遷偵測整體精度達到90%以上，SPOT衛星影像變遷偵測整體精度達到80%以上。因此預期多時期衛星影像，以複性複合模式作為變遷偵測方式，不失為一個可實際應用的方法。

摘要(英)

The usage of satellite images for land cover change detection has been an important task for environment monitoring. In this paper, we use multi-temporal satellite images and classifier to detect change regions. The classifier is Linear Mixing Model (LMM) with Least Square Orthogonal Subspace Projection (LSOSP). LMM is a model to descript classes in the image, and LSOSP is one of the methods to solution the LMM. It is proposed to detect the signal of the desired land-cover materials and eliminate the undesired signatures. Finally, an intensity image would be obtained to represent the intension of the desired signatures. However, this method cannot discriminate classes more than the number of bands of the combined image. Therefore, we proposed multi-level linear mixing model to solve this problem. The test data of this study include one simulation image and two SPOT4 satellite images. The overall accuracy is about 80%, and the kappa coefficient is about 0.6. Simulated data and real SPOT images are used for testing, and the results indicate that change detection using LMM is workable.

關鍵字(中)

★ 變遷偵測
★ 線性複合模式

關鍵字(英)

★ Linear Mixing Model
★ Change Detection

論文目次

ABSTRACT IV
目錄 V
圖目錄 VII
表目錄 IX
第一章緒論 1
1.1 前言 1
1.2 文獻回顧 2
1.2.1 影像相減法 3
1.2.2 影像比例法 5
1.2.3 分類後比較法 6
1.2.4 Chi-Square變遷偵測法 7
1.2.5 影像區塊分割變遷偵測法 8
1.2.6 主軸轉換分析法 10
1.2.7 多時期分類法 11
1.3 研究目的與方法概述 12
1.4 章節介紹 14
第二章線性複合模式 15
2.1 線性複合模式 16
2.2 最小二乘子空間投影法 22
2.2.1 材質訊號投影過程 23
2.3.2 正交子空間投影法 27
2.3.3 最大訊雜比 29
第三章線性複合模式於變遷偵測 33
3.1 單層次線性複合模式 34
3.1.1多時期影像合併 38
3.1.2類別決定及材質矩陣 39
3.1.3線性複合模式求解 40
3.1.4二元化影像 40
3.1.5 影像標定 40
3.2 多層次線性複合模式 42
3.2.1 多層次線性複合模式 42
3.2.2 多時期影像合併 44
3.2.3 類別決定 44
3.2.4材質群聚產生 45
3.2.5 線性複合模式求解 48
3.2.6 影像二元化及影像標記 49
3.2.7線性複合模式再求解 50
第四章測試及成果討論 52
4.1 模擬影像 53
4.1.1影像說明 53
4.1.2 測試成果 56
4.2 SPOT衛星影像 65
4.2.1 影像說明 65
4.2.2 SPOT影像Ⅰ 67
4.2.3 SPOT影像Ⅱ 73
4.3 成果討論 80
4.3.1 模擬影像 80
4.3.2 SPOT影像 81
第五章結論及未來展望 83
5.1 結論 83
5.2 建議 84
文獻回顧 86

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指導教授

陳繼藩(Chi-Farn Chen)

審核日期

2004-7-16

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