博碩士論文 966203005 詳細資訊




以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:10 、訪客IP:34.204.173.45
姓名 曹伶伶(Ling-Ling Tsao)  查詢紙本館藏   畢業系所 太空科學研究所
論文名稱 利用多光譜影像的光譜與空間資訊結合數學型態學進行海洋油汙偵測
(Using Spectral and Spatial Information Coupled with Mathematical Morphology for Oil Spill Detection in Multispectral Imagery)
相關論文
★ 基於GPU的SAR資料庫模擬器:SAR回波訊號與影像資料庫平行化架構 (PASSED)★ 高頻譜影像物質含量估計運用加權最小 平方法
★ 利用X光乳房攝影產生之紋理特徵影像在腫瘤偵測上之研究★ 高光譜影像雜訊模式估計
★ 利用高光譜影像作異常物偵測★ 無參數加權特徵萃取對遙測及醫學影像目標偵測的應用
★ 利用電腦自動化對數值高程模型作線形偵測★ 高光譜影像異常物偵測與識別之平行運算方法與其效能評估
★ 低解析度車牌視訊之強化與辨識★ 利用遙測影像自動萃取校正點
★ 新的影像融合演算法應用於多光譜遙測影像★ 利用影像處理進行遙測影像的河道偵測與醫學影像的血管偵測
★ 可調式都卜勒主動雷達校正器之改良研究★ 基於色彩校正的遙測影像變遷偵測
★ 利用固定式攝影機即時偵測土石流★ 藉由電腦視覺自動偵測土石流
檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   [檢視]  [下載]
  1. 本電子論文使用權限為同意立即開放。
  2. 已達開放權限電子全文僅授權使用者為學術研究之目的,進行個人非營利性質之檢索、閱讀、列印。
  3. 請遵守中華民國著作權法之相關規定,切勿任意重製、散佈、改作、轉貼、播送,以免觸法。

摘要(中) 近年來航海技術日趨發達,海上運輸及進出口貿易的次數也日漸頻繁,在這樣的情況下,海洋溢油汙染事件發生的機率也大幅提升,而這會對海洋生態環境造成嚴重的衝擊,因此如何偵測、監控並追蹤溢油汙染變成一項重要的議題。但由於在寬廣的海洋上,不易取得現地量測的資料,在這樣的情況下,能夠穩定且簡單獲取廣大地表資訊的遙測影像,提供海洋溢油汙染偵測良好的資訊。
在本次研究中,我們提出一套利用光學多光譜衛星影像來進行油汙偵測的技術。通常海洋溢油汙染分布只佔了衛星影像中的極小範圍,且油汙與海水的光譜特性有極大的不同,根據這樣的特性,我們將海洋油汙視為異常物質,並且針對影像進行異常物偵測(anomaly detection)。在執行異常物質偵測之後,可能為油汙的區域會明顯地顯示出來,但此偵測到的區域也同時包含了由海面波浪所造成的雜訊,為了要降低由海浪造成的誤差,依據油汙與波浪在影像中的分布型態明顯不同,我們計算影像的空間特徵資訊 (spatial feature information) 並加入流程之中來提升偵測結果。同時我們也引進了數學型態學 (mathematical morphology) 的概念,來更進一步地移除偵測結果中的雜訊。
我們所提出的偵測方法同時使用了影像的光譜資訊及空間特徵資訊,並且結合數學型態學,藉由這樣的方法,海洋油汙的分布能被正確地偵測出來。
摘要(英) Oil spill on the sea surface which is usually produced by human activities is disastrous to the ecological environment. In recent years, the technology for sea transportation is improvement. The export and import via marine transit are more and more frequently, the oil spill events are sometimes along with the process. How to detect, monitor and track the oil spill are always very important tasks. Due to oil spill often occur in open sea, remotely sensed image provides an effective technology to monitor the sea area. But the oil spill usually only occur in a very small area in the image. How to detect the oil spill on the sea surface is a challenge problem. Using the remotely sensed images to detect this unwelcomed hazard material on sea surface is a convenient and effective approach.
This study focuses on the detection of oil slick on sea surface using multi-spectral imagery technology. Since the oil spill area is usually very small compare to the sea area in the image scene, it can be considered as anomaly. By using anomaly detection algorithm, the oil spill can approximately figure out, but there are still some marine phenomena to interference the result. In order to discriminate oil slick from the other interferences, spatial features are introduced into anomaly detection. Furthermore, we also adopt mathematical morphology to filter through the maintain noise further. Therefore, our proposed method is to employ the spatial features of oil spill and combine with spectral information and mathematically morphologic operation to improve the oil spill detection. 
In the experiment, we adopt SPOT multispectral images for performance analysis.
關鍵字(中) ★ 數學型態學
★ 海洋溢油
★ 異常物偵測
★ 空間特徵資訊
關鍵字(英) ★ oil spill
★ anomaly detection
★ spatial feature
★ mathematical morphology
論文目次 Content
摘 要..................................................i
Abstract................................................iii
Content...................................................v
List of Figures........................................viii
List of Tables..........................................xii
Chapter 1 Introduction....................................1
Chapter 2 Anomaly Detection...............................4
2.1 RX Algorithm..........................................4
2.2 Expectation-Maximization Algorithm (EM Algorithm).....5
Chapter 3 Spatial Feature Information....................10
3.1 Gray Level Histogram.................................10
3.2 Texture Spectrum.....................................11
3.2.1 Black-White Symmetry (BWS).........................12
3.2.2 Geometric Symmetry (GS)............................12
3.2.3 Degree of Direction (DD)...........................13
3.2.4 Orientation Feature................................13
3.2.5 Central Symmetry (CS)..............................14
3.3 Texture Feature Coding Method........................14
3.3.1 Coarseness.........................................17
3.3.2 Homogeneity........................................18
3.3.3 Mean Convergence...................................18
3.3.4 Variance...........................................18
Chapter 4 Mathematical Morphology........................19
4.1 Erosion..............................................19
4.2 Dilation.............................................20
4.3 Opening..............................................20
4.4 Closing..............................................21
4.5 Reconstruction.......................................22
Chapter 5 Experimental Results...........................25
5.1 Date acquired........................................25
5.2 Methodology..........................................28
5.2.1 Spatial feature calculating........................31
5.2.2 Spatial Feature Selection..........................31
5.2.3 Maker and Mask for reconstruction..................37
5.3 Experiments with simulation data.....................38
5.3.1 Scenario I.........................................39
5.3.2 Scenario II........................................41
5.3.3 Scenario III.......................................43
5.3.4 Scenario IV........................................45
5.3.5 Scenario V.........................................47
5.4 Experiments with real data...........................50
5.4.1 Experiments with SPOT-1 data.......................50
Chapter 6 Conclusions....................................53
References...............................................54
Appendix A Figures of spatial feature information images.57
A.1 The spatial feature images of Test 1.................57
A.2 The spatial feature images of Test 2.................60
A.3 The spatial feature images of Test 3.................63
A.4 The spatial feature images of Test 4.................66
A.5 The spatial feature images of SPOT-1 image...........69
參考文獻 References
[1] Brown, C.E., Fingas, M.F., Goodman, R.H., Mullin, J.V., Choquet, M. and Monchalin, 2000. Airborne Oil Slick Thickness Measurement, in Proceedings of the Fifth International Conference on Remote Sensing for Marine and Coastal Environments, Environmental Research Institute of Michigan, Ann Arbor, Michigan, pp. I219-224.
[2] Chang, C.-I and S.-S Chiang, 2002. Anomaly Detection and Classification for Hyperspectral Imagery, IEEE transactions on geoscience and remote sensing, vol.40, No.6, pp. 1314-1325.
[3] Chen, H.T. (陳獻廷),2006。利用高光譜影像作異常物偵測,國立中央大學太空科學研究所碩士論文。
[4] Christoyianni, I., Dermatas, E. and Kokkinakis, G., 2000. Fast detection of masses in computer-aided mannography, IEEE Signal Processing Magazine, pp. 54-64.
[5] Del Frate, F., Petrocchi, A., Lichtenegger, J. and Calabresi, G., 2000. Neural Networks for Oil Spill Detection Using ERS-SAR Data. IEEE Transactions on Geosciences and Remote Sensing, Vol. 38, No. 5, pp. 2282-2287.
[6] Derrode, S. and Mercier, G., 2007, Unsupervised Multiscale Oil Slick Segmentation from SAR Images Using a Vector HMC Model, Pattern Recognition 40, 1135-1147.
[7] Fingas, M.F. and Brown C.E., 2000. Review of Oil Spill Remote Sensing, in Proceedings of SPILLCON, Australian Marine Safety Authority, Sydney, Australia.
[8] Huang, C.C. (黃俊忠),2006。利用X光乳房攝影產生之紋理特徵影像在腫瘤偵測上之研究,國立中央大學資訊工程研究所碩士論文。
[9] Knauss, J. A., 1996, Introduction to Physical Oceanography, Waveland Pr Inc.
[10] Kelly, E.J., 1989. Adaptive detection and parameter estimation for multidimensional signal models, MIT Lincoln Laboratory, MA, Tech. Rep., Apr., pp. 848.
[11] Liao, Y.C. (廖友千),2001。紋路特徵值分析應用於乳房X光片攝影之腫瘤偵測,國立成功大學資訊工程學系碩士論文。
[12] Liu, C.H. (劉建華),2007。台灣南部海域溢油動態資料庫-應用於海洋污染事故應變模擬分析,國立中央大學環境工程研究所碩士論文。
[13] Lopez, L., Moctezuma, M. and Parmiggiani, F., 2005. Oil spill detection using GLCM and MRF, IEEE, pp. 1781-1784.
[14] Plaza, J., R. Pérez, A. Plaza, P. Martínez and D. Valencia, 2005. Mapping Oil Spills on Sea Water Using Spectral Mixture Analysis of Hyperspectral Image Data, Proc. of SPIE Vol. 5995.
[15] Reed, I. and Yu X., 1990. Adaptive Multiple-Band CFAR Detection of an Optical Pattern with Unknown Spectral Distribution. IEEE transactions on acoustics. speech. and signal processing, Vol. 38, No. 4, pp. 1760-1770.
[16] Serra, J., 1982. Image Analysis and Mathematical Morphology. London, U.K.: Academic.
[17] Taylor, S, 1992. 0.45 to 1.1 μm Spectra of Prudhoe Crude Oil and of Beach Materials in Prince William Sound, Alaska, CRREL Special Report No. 92-5, Cold Regions Research and Engineering Laboratory, Hanover, New Hampshire, 14 p.
[18] Todd, K. M., 1996. The Expectation-Maximization Algorithm, IEEE Signal Processing Magazine, pp.47-60. November.
指導教授 任玄(Hsuan Ren) 審核日期 2009-7-13
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明