空間混沌模型於合成孔徑雷達影像變遷偵測之研究

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姓名

周念湘(Nien-Shiang Chou) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

空間混沌模型於合成孔徑雷達影像變遷偵測之研究
(A Study on Change Detection in SAR Images using Spatial Chaotic Model)

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摘要(中)

於本篇論文中，我們利用空間混沌模型(Spatial chaotic model)設計了一個新的變遷偵測演算法，用以辨認兩張同地點不同時間取得之合成孔徑雷達(SAR)影像的變異，以下簡稱SCM。此方法植基於具同調性的合成孔徑雷達影像可被塑模成空間混沌系統。在此研究中，偵測效能指標分成三類：辨識率(Detection rate)、誤判率(False detection rate)以及漏判率(Loss detection rate)；我們的方法都優於簡易影像相異法(Simple image difference)以及主成分分析法(Principle component analysis)。尤其在兩張前後期影像未完美疊合(mis-registration)，或者是變化細微的情況下，SCM的優勢更加明顯。在通常的SAR影像變遷偵測的方法中，去斑駁雜訊的前置程序是必要的，但也同時降低了影像的幾何特徵或者是空間解析度，進而降低辨識率；SCM不需要進行去斑駁的前置程序，其偵測率因而相對提高。

摘要(英)

In this thesis, we propose a new change detection algorithm for SAR images using the concept of spatial chaotic model. The new method was built on the fact that the coherent SAR images can be modeled by a spatial chaotic system. The proposed method was applied to multi-temporal polarimetric SAR images for change detections. As a reference, the simple image difference (DI) technique and the principal component analysis (PCA) were compared. Also, images mis-registration effects were also tested. The proposed method (hereafter called SCM) is capable of tolerating mis-registration effect even when the signal-to-noise ratio is relatively low, as compared to both DI and PCA methods. Comparison was made on the case when the radiometric changes are subtle. It is shown that the proposed method performs very well to detect such diminutive changes without being deteriorated by the presence of speckle for which both DI and PCA fail to carry out the detection.

關鍵字(中)

★ 碎型
★ 空間混沌模型
★ 合成孔徑雷達

關鍵字(英)

★ wavelet
★ fractal
★ change detection
★ fractional Brownian motion
★ SCM
★ SAR

論文目次

Abstract vi
Table of contents ix
1. Introduction 1
1.1. Research motivation 2
1.2. Main results 3
1.3. Outline of this thesis 4
2. Review of SAR signal model 7
2.1. SAR image formation 7
2.2. SAR speckle 11
2.2.1. Stochastically properties of SAR images 12
2.2.2. Chaotic phenomenon of SAR signals 14
3. Change detection approaches in SAR images 15
3.1. Image preprocessing 15
3.1.1. Radiometric correction 15
3.1.2. Image co-registration 16
3.1.3. Image de-speckling 16
3.2. Detection strategies 17
3.2.1. Supervised/Unsupervised approaches 17
3.2.2. Pixel-based/Feature-based approaches 18
3.2.3. Object-level/hybrid approaches 19
3.3. Thresholding methods 20
4. Review of statistics 23
4.1. Statistical Inference 23
4.1.1. Maximum-Likelihood (ML) Estimation 25
4.1.2. Maximum-a-Posteriori (MAP) Estimation 26
4.1.3. Neyman–Pearson decision rule (CFAR) 26
4.2. Parzen window Density Estimation 28
4.2.1.1. Choice of bandwidth 33
4.2.1.2. Choice of kernel 34
4.2.1.3. Parzen-Window Convergence 35
5. A study of fractals and fractional Brownian motion 37
5.1. Fractals 37
5.1.1. Deterministic fractals 37
5.1.2. Random fractals 39
5.1.3. Linear iterated function systems (IFS) 39
5.1.3.1. Example 1: leaf 40
5.1.3.2. Example 2: mountain 41
5.2. Regular Brownian motion 42
5.3. Fractional Brownian motion 43
5.3.1. Time-domain properties of fBm 46
5.3.2. Spectral-domain properties of fBm 48
5.4. Characterizing chaos 51
5.4.1. Dimension estimation 52
5.4.1.1. Box counting dimension 52
5.4.1.2. Correlation dimension 52
5.4.2. Attractor reconstruction methods 54
5.4.2.1. Example: Lorenz system 55
5.4.2.2. Choice of time delay 57
5.4.2.3. Choice of embedding dimension 59
5.5. Simulation of fractional Brownian motion 60
5.5.1. Simulation by time domain formula 60
5.5.2. Simulation by spectral domain properties 61
6. The proposed method 69
6.1. Chaos and nonlinear dynamics 69
6.2. Local fractal dimension of an image 73
6.2.1. The power spectral density of the fBm 73
6.2.2. The variance of the wavelet coefficients 75
6.3. Spatial chaotic model 75
6.4. Fractal image derived from a SAR image 77
6.5. Change detection by constant false alarm rate (CFAR) value 78
7. Performance evaluation 81
7.1. Methods compared 81
7.2. Performance index 81
7.3. SCM validation with simulated data 81
7.3.1. Case-A1 85
7.3.2. Case-A2 87
7.3.3. Case-B1 89
7.3.4. Case-B2 91
7.3.5. Discussion of case-A1, A2, B1, and B2 92
7.3.6. Case-C1 94
7.3.7. Case-C2 96
7.3.8. Discussion of case-C1, and C2 97
7.4. Study area and data sets 98
7.5. Ground truth 103
7.6. Results and Discussion 103
8. Conclusions 108
9. Future works 109
10. References 111

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指導教授

陳錕山、范國清
(Kun-Shan Chen、Kuo-Chin Fan)

審核日期

2010-7-25

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