| 研究生: |
黃弘毅 Hong-Yi Huang |
|---|---|
| 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 |
| 指導教授: | 蔡富安 |
| 口試委員: | |
| 學位類別: |
碩士 Master |
| 系所名稱: |
工學院 - 土木工程學系 Department of Civil Engineering |
| 論文出版年: | 2019 |
| 畢業學年度: | 107 |
| 語文別: | 中文 |
| 論文頁數: | 101 |
| 中文關鍵詞: | 道路萃取 、分類 、紋理特徵 |
| 外文關鍵詞: | Road extraction, Classification, Texture features |
| 相關次數: | 點閱:9 下載:0 |
| 分享至: |
| 查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報 |
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%。
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.
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.