臺灣博碩士論文加值系統

目標物點狀散射點特徵在合成孔徑雷達目標物影像辨識上非常重要，因此找尋合適的萃取方法就十分關鍵，目標物點狀特徵的萃取原則在於維持主波瓣的強度，並降低旁波瓣的影響，並且能有效反應真實目標物散射點位置。藉由這些資訊我們可以有效利用在目標物辨識、分類上。其中本研究以增顯目標物點狀散射點為主要目標。
合成孔徑雷達影像依取像方式可分成兩類，焦點式和掃描式。本研究主要是由M. Çetin 所提出針對焦點式合成孔徑雷達增顯地表反射係數的演算邏輯為基礎，其設計理念是以回波訊號方程式為基礎，納入強烈假設的相加性雜訊在演算的模型上，並以雷達回波訊號的指數項，建構成為演算法之轉換核心矩陣，再針對這個模型作最佳化的運算。為了將原有限定在焦點式合成孔徑雷達的地表參數運算擴張到掃描式合成孔徑雷達的資料上，本研究以掃描式的訊號接收數學式重新建構出一個新的演算法核心矩陣，並將散射點增顯後的結果簡化成目標物點狀散射點相對位置的圖形，方便未來研究上的應用。
本研究使用調控演算法(Regularization Algorithm)增顯散射點特徵，其好處在於可以視使用者的需要來增加代價方程式(Cost function)的二次或非二次項，由於合成孔徑雷達的資料為複數資料，求解過程以複數共軛梯度為主要概念並配合準牛頓演算法(Quasi-Newton iteration)和柯列斯基分解(Cholesky decomposition)處理最佳化的問題。由於使用兩種資料形式的不同，本研究試著將掃描式的處理以切割的方式來完成，並先尋找背景的標準差為區分背景區域和不標物區域的準則，防止區域極大值的產生，並增加演算的速度，因此配合數個地真調查的目標物在數個參數上重新定義，本研究使用RADARSAT高解析度掃描式合成孔徑雷達影像為測試資料，並配合地真調查的結果來佐證經過演算法增顯出的目標物點狀散射點特徵，並也使用NASA/JPL AIRSAR全偏極資料為第二組例子。
其後會與傳統方法MV(Minimum variance)和MUSIC (Multiple Signal Classification)的效能作比較，展示散射點增強的結果並使用評鑑指標如目標物對背景混亂物比、主波瓣3dB寬和實際運算時間，評鑑效能且列表表示。結果顯示在經過規則化適當調整演算法參數之後，無論是在評鑑指標或是在統計機率密度分佈圖上，目標點旁波瓣的抑制和目標物與背景分離上皆有明顯的效果。

Target identification and recognition of SAR images require good feature selection and enhancement. Due to the coherent process, it is difficult to discriminate the SAR target feature properties simply using the shape, shadow, tone, color and texture, to name a few. The scattering center is one of the important properties for extracting the SAR feature. This involves working on raw data (amplitude and phase) as part of image formation. In this paper, we modified an algorithm based a conjugate gradient (CG) optimum method originally proposed for spot mode SAR images, in order to enhance the Stripmap mode SAR targets. The SAR image reconstruction method for spotlight mode uses the range profile data that displays on spatial frequency domain in polar form. By the conventional method it is resampled to be in rectangular form, followed by using inverse Fourier transform with properly weighted window. In papers the recompiling method is done by introducing a projection operator kernel. Directly applying CG method, one has to change the data format to range profile of polar form but suffer from information loss in this recompiling processing. To avoid this information degradation, it has been suggested to replace SAR projection operator kernel by Fourier transform kernel, and let it have suitability for Stripmap mode data.
To validate the effectiveness and efficiency of the modified method, a series of RADARSAT SAR images at fine mode were tested with ground truth available overpass the image acquisition. In addition to, we import the fully polarization SAR data from NASA/JPL AIRSAR acquired from south Taiwan when Sep. 27, 2000. We also compared the performance with MV (Minimum Variance) and MUSIC (Multiple Signal Classification) methods. Performance indices include target to clutter ratio, 3 dB mainlobe width and CPU time. From the logarithmic probability density distribution and column ordering plot of enhanced image and original image, it was demonstrated that modified method provides the best performance among the three methods besides CPU time.

第1章 簡介 1
1.1 目標物偵測與辨識 2
1.2 文獻回顧與本研究方法概述 4
1.3 論文的整體架構 7
第2章 合成孔徑雷達原理與一般萃取特徵 8
2.1 焦點式合成孔徑雷達 9
2.2 掃描式合成孔徑雷達 13
2.3 常使用的雷達目標務特徵 17
2.3.1 目標物和陰影面積 17
2.3.2 平均強度比(Mean ratio) 18
2.3.3 目標物方位角 18
2.3.4 長短軸比 20
第3章 演算模型的產生 21
3.1 調控演算法(Regularization Algorithm) 21
3.2 基於模型的非二次調控演算法 24
3.3 準牛頓演算法的應用 26
3.4 演算法核心之建置 28
3.4.1 焦點式合成孔徑雷達的建構結果 28
3.4.2 掃描式合成孔徑雷達的建構結果 31
第4章 最佳化演算法 38
4.1 演算法的限制 38
4.2 柯列斯基分解(Cholesky decomposition)解線性系統 40
4.2.1 柯列斯基分解 40
4.2.2 柯列斯基分解解線性系統 42
4.3 精確數值疊代的限制 44
4.4 參數的設定 45
4.4.1 目標物區域中心的斜距距離 46
4.4.2 建議的λ值 48
4.5 影像切割 49
4.6 整合全部演算法的流程 52
第5章 測試資料與成果展示 55
5.1 評估方法 55
5.1.1 目標物與背景混亂物比 55
5.1.2 主波瓣3 分貝寬 57
5.1.3 傳統目標物增強演算法 58
5.2 RADARSAT資料形式 61
5.3 NASA/JPL AIRSAR資料形式 66
5.4 實測資料的增強結果 68
5.4.1 單偏極且有地真調查的RADARSAT目標物 68
5.4.2 多偏極未經地真調查的AIRSAR目標物 76
5.5 測試結果的評鑑 80
第6章 結論與未來展望 83
6.1 結論與心得 83
6.2 未來展望 83
參考文獻 85

[1].Oliver C. and S. Quegan,; ”Understanding Synthetic Aperture Radar Images,” Boston Artech House, 1998.
[2].Brown W. M., G. G. Houser and R. E. Jenkins,; “Synthetic Aperture Processing with Limited Storage and Presumming,” Transactions on Aerospace and Electronic Systems, IEEE, Vol. 9, 1973, pp. 166 – 176.
[3].Brown W. M.,; “Synthetic Aperture Radar,” Transactions on Aerospace and Electronic Systems, IEEE, Vol. 3, 1967, pp. 217 – 229.
[4].Keydel E. R., S. Wu Lee and J. T. Moore,; “MSTAR Extended Operating Conditions,” SPIE, 2757, pp-228-242, 1996.
[5].Brito A. E., S. H. Chan and S. D. Cabrera,; “SAR Image Formation Using 2-D Re-Weighted Minimum Norm Extrapolation,” Algorithm for Synthetic Aperture Radar Imagery VI, SPIE. , Vol. 3721, 1999, pp. 78 – 91.
[6].DeGraaf S. R.,; “SAR Imaging via Modern 2-D Spectral Estimation Methods,” Image Processing , IEEE Trans. , Vol. 7, 1998, pp. 729 – 761.
[7].Zhang J., J. Schroeder and N. J. Redding,; “SAR image enhancement for small target detection,” Acoustics, Speech, and Signal Processing, IEEE Int. Conf., Vol. 3, 2003, pp. 449 – 452.
[8].Odendaal J. W., E. Barnard and C. W. I Pistorius.,; “Two-Dimensional Superresolution Radar Imaging Using the MUSIC Algorithm,” Antennas and Propagation, IEEE Trans. , Vol. 42, 1994, pp. 1386 – 1391.
[9].Quinquis A., E. Radoi and F. C. Totir.,; “Some Radar Imagery Results Using Superresolution Techniques,” Transactions on Antennas and Propagation, IEEE, Vol. 52, 2004, pp. 1230 – 1244.
[10].DeGraaf S. R.,; “Parametric Estimation of Complex 2-D Sinusoids,” Spectrum Estimation and Modeling, Fourth Annual ASSP Workshop, 1988, pp. 391 – 396.
[11].Çetin M. and W. C. Karl,; “Feature-Enhanced Synthetic Aperture Radar Image Formation Based on Nonquadratic Regularization,” Image Processing, IEEE Trans. , Vol. 10, 2001, pp. 623 – 631.
[12].Çetin M.,; “Feature-Enhanced Synthetic Aperture Radar Imaging,” Ph.D. thesis, Boston University, Feb. 2001.
[13].Yeo T. S., N. L. Tan and C. B. Zhang,; “A Stripmap to Spotlight Data Converting Algorithm,” Geoscience and Remote Sensing Symposium Proceedings, IEEE Int. IGARSS '98 Conf., Vol 2, 1998, pp. 1168 – 1170.
[14].Soumekh M.,; “Digital Spotlight and Coherent Subaperture Image Formation for Stripmap Synthetic Aperture Radar,” Image Processing. ICIP-94., IEEE Int. Conf. Vol 1, 1994, pp. 476 – 480.
[15].Soumekh M.,; “Synthetic Aperture Radar Signal Processing with MATLAB Algorithms,” New York J. Wiley, 1999.
[16].Jakowatz Jr. C. V., D. E. Wahl, P. H. Eichel, D. C. Ghiglia, and P. A. Thompson,; ”Spotlight-mode Synthetic Aperture Radar : a Signal Processing Approach,” Kluwer Academic Publishers, Norwell, MA, 1996.
[17].Carrara W. G., R. S. Goodman and R.M. Majewski,; “Spotlight Synthetic Aperture Radar : Signal Processing Algorithm,” Boston, MA; Artech House, 1995.
[18].Ross T., S. Worrell, V. Velten, J. Mossing and M. Bryant,; ”Standard SAR ATR Evaluation Experiments using the MSTAR Public Release Data Set,” SPIE, 3370, pp-566-573, 1998.
[19].Kuo J. M., and K. S. Chen.,; “Ship Wake Detection in SAR Images Using Combination of Wavelets Correlator and Radon Transform,” Optical Engineering (SCI), vol. 41, pp-686-696, 2002.
[20].Golub G. H. and C. F. Van Loan,; “Matrix Computations,” Baltimore : Johns Hopkins University Press, 1996.
[21].Sun B.,; “Feature Enhanced Regularized Reconstruction Algorithm for SAR,” Neural Networks and Signal Processing, IEEE Int. Conf., 2003, pp. 969 – 970.
[22].Press W. H., S. A. Teukolsky, W. T. Vetterling and B. P. Flannery,; “Numerical Recipes in FORTRAN : The Art of Scientific Computing,” New York, NY, USA Cambridge University Press, 1992.