利用高解析衛星影像萃取及重建道路路網

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：29

、訪客IP：3.144.172.115

姓名

黃弘毅(Hong-Yi Huang) 查詢紙本館藏

畢業系所

土木工程學系

論文名稱

利用高解析衛星影像萃取及重建道路路網
(Road network extraction and reconstruction using high resolution satellite imagery)

相關論文

★ 三維房屋模型實景紋理影像製作與敷貼之研究	★ 紋理輔助高解析度衛星影像分析應用於偵測入侵性植物分布之研究
★ 利用高光譜影像偵測外來植物-以恆春地區銀合歡為例	★ 以視訊影像進行三維房屋模型實景紋理敷貼之研究
★ 區塊式Level of Detail地景視覺模擬之研究	★ 高光譜影像立方體紋理特徵之三維計算
★ 漸變式多重解析度於大型地景視覺模擬之應用	★ 區塊式LOD網格細化於大型地形視覺模擬之應用
★ 多層次精緻度三維房屋模型之建置	★ 高光譜影像立方體於特徵空間之三維紋理計算
★ 影像修補技術於牆面紋理影像遮蔽去除之應用	★ 結合遙測影像與GIS資料以資料挖掘技術進行崩塌地辨識-以石門水庫集水區為例
★ 利用近景影像提高三維建物模型之細緻化等級	★ 以地面及空載光達點雲重建複雜物三維模型
★ 高精緻度房屋模型結合蟻群演算法於室內最佳路徑選擇之應用	★ 二次微分法於空載全波形光達之特徵萃取與地物分類

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]

本電子論文使用權限為同意立即開放。
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。
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)

道路是各種車輛與行人通行的主要基礎設施之一，研究道路圖資也是遙測領域所著重的項目之一。本研究利用高解析衛星影像，萃取道路資訊，並建立初步的道路路網資訊。
本研究開發出一套系統化的流程，可從高解析衛星影像中萃取道路資訊並重建道路路網。此程序主要可分為兩個階段:第一階段利用多光譜影像之光譜資訊與灰階共生矩陣(GLCM)演算法解算的紋理資訊，並以支持向量機分類演算法，進行影像分類萃取出道路像元。第二階段則利用雷登轉換進行線性特徵萃取，並進行道路像元的線追蹤，進而重建道路路網。
本研究以 Pleiades 高解析度衛星影像為主要資料，並以台北市部份地區為研究區域。結合前述的兩項主要流程，能有效的偵測測試區域中影像的道路區域，並量化精度與評估演算法的可靠性。在驗證演算法的成果過程中，評估了目標道路萃取的完整性與中心線位置準確性。而整個過程又分為三大步驟:(1)整體的分類正確性 (2)萃取的完整性，以及(3)道路中心線精度。實驗結果顯示，整體的分類正確性達到 93%，道路路網完整性達到 86%。

摘要(英)

Automatic road extraction from remote-sensing imagery plays an important role in many applications. In general, road extraction from remote-sensing imagery can be considered as a classification process in which pixels are divided into road classes and others. This study develops a systematic procedure using high-resolution satellite imagery to extract and reconstruct road network in urban areas with a road width greater than 8 meters.
The developed procedure for extracting roads and reconstructing the network can be divided into three steps. The first step; obtaining texture parameters, including contrast, entropy and homogeneity of each pixel using gray level co- occurrence matrix (GLCM). Afterwards, the support vector machine (SVM) is used as a classifier to identify road pixels. Finally, linear feature detection of the road network with radon transform is performed.
This study uses high resolution Pleiades satellite imagery which covers an area of 8 km2 of Taipei city, Taiwan. Experiment results show that the proposed procedure and method can achieve an overall accuracy of 93.11% with a kappa coefficient of 89.20% and completeness over 85%. The results indicate the proposed method is efficient to extract road network from high resolution satellite images.

關鍵字(中)

★ 道路萃取
★ 分類
★ 紋理特徵

關鍵字(英)

★ Road extraction
★ Classification
★ Texture features

論文目次

中文摘要..................................................................................................III
ABSTRACT.............................................................................................. II
目錄..........................................................................................................III
圖目錄................................................................................................... VIII
表目錄.....................................................................................................XII
第一章緒論 .............................................................................................. 1
1.1 研究背景.......................................................................................... 1
1.2 研究動機與目的............................................................................ 2
1.3 論文架構.......................................................................................... 4
第二章文獻回顧...................................................................................... 5
2.1 道路萃取.......................................................................................... 5
2.1.1 知識法(KNOWLEDGE-BASED METHODS) .............................. 6
2.1.2 數學形態學(MATHEMATICAL MORPHOLOGY)..................... 7
2.1.3 主動輪廓模型(ACTIVE CONTOUR MODEL) ............................ 8
2.1.4 分類法(CLASSIFICATION-BASED METHODS) ....................... 9
V
2.1.5 小結................................................................................................ 11
2.2 道路中心線萃取............................................................................ 12
2.3 總結................................................................................................ 13
第三章研究方法.................................................................................... 15
3.1 研究方法綜述................................................................................ 15
3.2 資料介紹........................................................................................ 16
3.2.1 PLEIADES 衛星影像.................................................................... 16
3.3 道路萃取........................................................................................ 18
3.3.1 NDVI 植物區濾除......................................................................... 19
3.3.2 紋理分析........................................................................................ 21
3.3.3 支持向量機.................................................................................... 26
3.4 道路路網重建................................................................................ 27
3.4.1 前處理............................................................................................ 29
3.4.2 線性特徵萃取................................................................................ 30
3.4.3 道路路網連結................................................................................ 36
第四章實驗成果與分析........................................................................ 39
VI
4.1 紋理分析........................................................................................ 39
4.2 分類訓練及檢核............................................................................ 42
4.2.1 訓練資料........................................................................................ 42
4.2.2 分類成果........................................................................................ 44
4.3 道路路網重建................................................................................ 47
4.3.1 第一階段萃取................................................................................ 48
4.3.2 第二階段萃取................................................................................ 54
4.3.3 路網連結........................................................................................ 56
4.4 道路路網重建成果評估................................................................ 59
4.4.1 道路路網完整性評估.................................................................... 62
4.4.2 漏授誤差........................................................................................ 63
4.4.3 誤授誤差........................................................................................ 69
4.4.4 道路路網中心線精度評估 ........................................................... 72
第五章總結與建議................................................................................ 78
5.1 研究總結........................................................................................ 78
5.2 研究建議........................................................................................ 79
VII
參考文獻.................................................................................................. 81

參考文獻

周明中，2005，紋理輔助高解析度衛星影像應用於偵測入侵性植物分布之研究，碩士論文，國立中央大學土木工程研究所。
賴哲儇，2009，高光譜影像立方體於特徵空間之三維紋理計算，碩士論文，國立中央大學土木工程研究所。
Anil, P. N., & Natarajan, S., 2010. A novel approach using active contour model for semi-automatic road extraction from high resolution satellite imagery. In2010 Second International Conference on Machine Learning and Computing, pp. 263-266.
Bakhtiari, H. R. R., Abdollahi, A., & Rezaeian, H., 2017. Semi automatic road extraction from digital images. The Egyptian Journal of Remote Sensing and Space Science, Vol. 20, No. 1, pp. 117-123.
Baraldi, A., & Parmiggiani, F., 1995. An Investigation of the Textural Characteristics Associated with Gray Level Cooccurrence Matrix Statistical Parameters. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, Vol. 33, No. 2.
Curran, P. J., 1988. The semivariogram in remote sensing: an introduction. Remote sensing of Environment, Vol. 24, No. 3, pp. 493-507.
Grote, A., Heipke, C., & Rottensteiner, F., 2012. Road network extraction in suburban areas. The Photogrammetric Record, Vol. 27, No. 137, pp. 8-28.
Haralick, R. M., Shanmugan, K., and Dinstein, I., 1973. Texture Features for Image Classification, IEEE Trans. Sys. Man Cyber, Vol. 3, No. 6, pp. 610- 621.
82
Hu, X., Tao, C. V., & Hu, Y., 2004. Automatic road extraction from dense urban area by integrated processing of high resolution imagery and lidar data. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 35, pp. 288-292.
Hu, J., Razdan, A., Femiani, J. C., Cui, M., & Wonka, P., 2007. Road network extraction and intersection detection from aerial images by tracking road footprints. IEEE Transactions on Geoscience and Remote Sensing, Vol. 45, No. 12, pp. 4144-4157.
Huang, X., & Zhang, L., 2009. Road centreline extraction from high‐resolution imagery based on multiscale structural features and support vector machines. International Journal of Remote Sensing, Vol. 30, No. 8, pp. 1977-1987.
Hughes, G., 1968. On the mean accuracy of statistical pattern recognizers. IEEE transactions on information theory, Vol. 14, No. 1, pp. 55-63.
Kirthika, A., & Mookambiga, A., 2011. Automated road network extraction using artificial neural network. In 2011 International Conference on Recent Trends in Information Technology (ICRTIT), pp. 1061-1065.
Liu, R., Miao, Q., Song, J., Quan, Y., Li, Y., Xu, P., & Dai, J., 2019. Multiscale road centerlines extraction from high-resolution aerial imagery. Neurocomputing, Vol. 329, pp. 384-396.
Ma, R., Wang, W., & Liu, S., 2012. Extracting roads based on Retinex and improved Canny operator with shape criteria in vague and unevenly illuminated aerial images. Journal of Applied Remote Sensing, Vol. 6, No. 1, p. 063610.
Marceau, D. J., Howarth, P. J., Dubois, J. M. M., & Gratton, D. J., 1990.
83

Evaluation of the grey-level co-occurrence matrix method for land-cover classification using SPOT imagery. IEEE Transactions on Geoscience and Remote Sensing, Vol. 28, No. 4, pp. 513-519.
Maurya, R., Gupta, P. R., & Shukla, A. S., 2011. Road extraction using k-means clustering and morphological operations. In 2011 International Conference on Image Information Processing, pp. 1-6.
Muneer, T., & García, I. I., 2017. The automobile. In Electric Vehicles: Prospects and Challenges, pp. 1-91.
Murphy, L. M., 1986. Linear feature detection and enhancement in noisy images via the Radon transform. Pattern recognition letters, Vol. 4, No. 4, pp. 279- 284.
Pal, M., & Mather, P. M., 2005. Support vector machines for classification in remote sensing. International Journal of Remote Sensing, Vol. 26, No. 5, pp. 1007-1011.
Pehani, P., Čotar, K., Marsetič, A., Zaletelj, J., & Oštir, K., 2016. Automatic geometric processing for very high resolution optical satellite data based on vector roads and orthophotos. Remote Sensing, Vol. 8, No. 4, p. 343.
Poullis, C., & You, S., 2010. Delineation and geometric modeling of road networks. ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 65, No. 2, pp. 165-181.
Shen, J., Lin, X., Shi, Y., & Wong, C., 2008. Knowledge-based road extraction from high resolution remotely sensed imagery. In 2008 Congress on Image and Signal Processing, Vol. 4, pp. 608-612.
Shi, W., Miao, Z., & Debayle, J., 2014. An integrated method for urban main-road centerline extraction from optical remotely sensed imagery.IEEE
84

Transactions on Geoscience and Remote Sensing, Vol. 52. No.6, pp. 3359-
3372.
Song, M., & Civco, D., 2004. Road extraction using SVM and image
segmentation. Photogrammetric Engineering & Remote Sensing, Vol. 70,
No. 12, pp. 1365-1371.
Toft, P. A., & Sørensen, J. A., 1996. The Radon transform-theory and
implementation.
Wang, L., & He, D. C., 1990. A new statistical approach for texture
analysis. Photogrammetric Engineering and Remote Sensing, Vol. 56, No. 1,
pp. 61-66.
Wang, J., Qin, Q., Yang, X., Wang, J., Ye, X., & Qin, X., 2014. Automated road
extraction from multi-resolution images using spectral information and texture. In 2014 IEEE Geoscience and Remote Sensing Symposium, pp. 533- 536.
Wang, W., Yang, N., Zhang, Y., Wang, F., Cao, T., & Eklund, P., 2016. A review of road extraction from remote sensing images. Journal of traffic and transportation engineering, Vol. 3, No. 3, pp. 271-282.
Wiedemann, C., Heipke, C., Mayer, H., & Jamet, O., 1998. Empirical evaluation of automatically extracted road axes. Empirical evaluation techniques in computer vision, pp. 172-187.
Zhang, Q., & Couloigner, I., 2007. Accurate centerline detection and line width estimation of thick lines using the radon transform. IEEE Transactions on Image Processing, Vol. 16, No. 2, pp. 310-316.
Zhao, L., & Wang, X., 2010. Road extraction in high resolution remote sensing images based on mathematic morphology and snake model. In 2010 3rd
85

International Congress on Image and Signal Processing, Vol. 3, pp. 1436- 1440.

指導教授

蔡富安

審核日期

2019-8-19

推文