题名

應用彩色航照以物件導向影像分析方法進行崩塌地及鄰近地物之分類

并列篇名

Application of Aerial Ortho-Imagery with Object-Based Image Analysis to Classify Landslide and Neighbor Surface Features

DOI

10.6342/NTU201602964

作者

鄭傅謙

关键词

彩色正射航照 ； 物件式 ； 影像判釋 ； 半自動化 ； Orthorectified aerial photographs ； Object-based ； Image interpretation ； Semi-automated

期刊名称

國立臺灣大學土木工程學系學位論文

卷期/出版年月

2016年

学位类别

碩士
导师

李鴻源
内容语文

繁體中文
中文摘要

近年來受到全球暖化的影響，使得颱風、洪水等極端氣候出現的頻率增加且強度增強，造成山區每逢颱風、雨季的侵擾，常有山崩、土石流等災情發生，進而導致人民生命財產的損失。因此如何建立一套完善、具有可操作性的規範，以做為分析環境變遷與災害防治的知識基礎，便是獲得大量高精度資料之後所需面臨的問題。本研究根基於現有知識，建立簡明之綜合應用方法與規範，期望能以明確的流程提升遙測資料應用於災防政策之具體成效。 在遙感探測領域當中，過去已經有許多研究藉由衛星影像進行崩塌地的自動偵測，然而受限於解析度的不足造成精度亦受局限。本研究將以常見的彩色正射航照為主要資料並搭配數值地形模型(DTM)進行影像的自動分類，除了光譜所提供的資訊之外，還結合空間資訊來進行分析，協助區分光譜不易分辨的地物。在影像分類方法上，採用物件式影像分析法將像元區塊化，除了能減少高解析度影像在進行分類時常產生之椒鹽效應的問題，物件式分析也更接近人類視覺上對物體的認知，能夠藉由地物的紋理、形狀以及在空間中的相對位置等特性來進行影像的分類。研究結果顯示，影像判釋整體精確度能達到87%以上，且具有穩定、泛用、快速的特性，藉由半自動化影像分析流程，能夠大大減少人工程序，以增加影像大量處理的能力。
英文摘要

Recently affected by global warming, it leads to typhoons, floods and other extreme weather occurring more frequency and seriously. During the typhoon and rainy season, it occurs the landslides, debris flow and other disasters more frequently, which making people lose of life and property. Therefore, how to establish the sound and controllable specification as a basis to analyze environmental change and disaster prevention is the problem required to face after getting a large number of high resolution remote sensing data. In this study, based on present knowledge, it builds a simple application of methods and specification. We expect to enhance the application to disaster prevention policies performance. In the past, analysis satellite images by using remote sensing technique has been a major method for detecting landslide among the field of remote sensing. Due to the insufficient degree of spatial resolution, the accuracy of landslides detection is restrained. This study will mainly use the common orthorectified aerial photographs, and digital terrain model (DTM) to automatically classify the images. These provide not only the information of the spectrum, but also combine spatial information to analyze and assist to spectrum which not easy to distinguish objects. In the image classification method, applying Object-based Image Analysis (OBIA) is to process aerial photographs. Besides reducing classify high-resolution images arising the problem of salt and pepper effect, the object are closer to human visual perception. It can be texture, shape, relative position in space and other features to classify images. The results showed that the overall accuracy of the image interpretation can be up to over 87%, meanwhile it is stable, generic, fast. The semi-automated image analysis processing can greatly reduce the time of artificial processing and increase the capacity of handling the large number of images.
主题分类 工學院 > 土木工程學系
工程學 > 土木與建築工程
参考文献
  1. 1. 孔繁恩 (2012) 物件導向分類法於DMC航照影像萃取崩塌地之研究。碩士論文，國立政治大於地政研究所。
    連結：
  2. 2. 尹孝元、梁隆鑫、陳錕山、黃珮琦 (2010) 衛星影像於國土變異監測之應用。航測及遙測學刊，15(1)，65-78。
    連結：
  3. 3. 王韻皓、陳連晃 (2011) 農業委員會林務局農林航空測量所業務介紹。地籍測量:中華民國地籍測量學會會刊，30(1)，38-46。
    連結：
  4. 4. 白林奇 (2008) 應用衛星影像於花東土砂災害集水區崩塌地潛感分布及地景指標之研究。碩士論文，國立屏東科技大學森林系碩士班。
    連結：
  5. 6. 洪凱政 (2009) 應用多光譜影像多種特徵偵測崩塌地之研究。碩士論文，國立成功大學測量及空間資訊學系。
    連結：
  6. 7. 張崑宗、劉進金 (2006) 利用類神經網路方法於高解析衛星影像及地形資料之崩塌地辨識-以九份二山為例。航測及遙測學刊，11(2)，161-174。
    連結：
  7. 8. 莊心凱 (2012) 結合地貌主題圖層及物件式影像分析方法應用於山區氾濫原及周邊區域特徵判釋。碩士論文，國立台灣大學土木工程學研究所。
    連結：
  8. 9. 莊智瑋、林昭遠、陳正湘 (2009) 利用紋理因子改善影像分類準確度之研究。水土保持學報，41(2)，153-168。
    連結：
  9. 11. 陳樹群、吳俊鋐 (2009) 高雄縣小林村獻肚山巨型深層崩塌引致之地形變遷特性。中華水土保持學報，40(4):，359-376。
    連結：
  10. 12. 黃韋凱、林銘郎、陳良健、林彥享、蕭震洋 (2010) 物件導向分析方法應用於遙測影像之分區及崩塌地與人工設施分類。航測及遙測學刊，15(1)，29-49.
    連結：
  11. 13. 董炤巖 (2010) 以物件導向分類法進行SPOT衛星影像之崩塌地萃取。碩士論文，屏東科技大學森林系。
    連結：
  12. 14. 詹勳全、張嘉琪、陳樹群、魏郁軒、王昭堡、李桃生 (2015) 台灣山區淺層崩塌地特性調查與分析。中華水土保持學報，46(1)，19-28。
    連結：
  13. 15. 劉守恆 (2002) 衛星影像於崩塌地自動分類組合之研究。碩士論文，國立成功大學地球科學研究所。
    連結：
  14. 16. 鄭智仁 (2010) 應用空載光達與正射影像以物件導向影像分析法進行自動化崩塌地偵測。碩士論文，國立成功大學衛星資訊暨地球環境研究所。
    連結：
  15. 18. 譚克平 (2008) 極端值判斷方法簡介。臺東大學教育學報，19(1)，131-150。
    連結：
  16. 20. Barbosa, M. P., Singhroy, V., & Saint-Jean, R. (1999). Mapping coastal erosion in Southern Paraíba, Brazil from RADARSAT-1. Canadian Journal of Remote Sensing, 25(3), 323-328.
    連結：
  17. 21. Chu, H. J. (2014). Spatiotemporal analysis of vegetation index after typhoons in the mountainous watershed. International Journal of Applied Earth Observation and Geoinformation, 28, 20-27.
    連結：
  18. 22. Ciampalini, A., Raspini, F., Bianchini, S., Frodella, W., Bardi, F., Lagomarsino, D., ... & Onori, R. (2015). Remote sensing as tool for development of landslide databases: The case of the Messina Province (Italy). geodatabase.Geomorphology, 249, 103-118..
    連結：
  19. 23. Drăguţ, L., Csillik, O., Eisank, C., & Tiede, D. (2014). Automated parameterisation for multi-scale image segmentation on multiple layers. ISPRS Journal of Photogrammetry and Remote Sensing, 88, 119-127.
    連結：
  20. 24. Dymond, J. R., Shepherd, J. D., Clark, H., & Litherland, A. (2006). Use of VEGETATION satellite imagery to map pasture quality for input to a methane budget of New Zealand. International Journal of Remote Sensing, 27(6), 1261-1268.
    連結：
  21. 25. Feng, J., Jiao, L., Liu, F., Sun, T., & Zhang, X. (2016). Unsupervised feature selection based on maximum information and minimum redundancy for hyperspectral images. Pattern Recognition, 51, 295-309.
    連結：
  22. 26. Fookes, P. G., Sweeney, M., Manby, C. T., & Martin, R. P. (1985). Geological and geotechnical engineering aspects of low-cost roads in mountainous terrain.Engineering Geology, 21(1-2), 1-152.
    連結：
  23. 27. Gao, Y., Mas, J. F., Kerle, N., & Navarrete Pacheco, J. A. (2011). Optimal region growing segmentation and its effect on classification accuracy. International journal of remote sensing, 32(13), 3747-3763.
    連結：
  24. 28. Hay, G. J., Niemann, K. O., & McLean, G. F. (1996). An object-specific image-texture analysis of H-resolution forest imagery. Remote Sensing of Environment, 55(2), 108-122.
    連結：
  25. 29. Hossain, M. A., Jia, X., & Pickering, M. (2012, July). Improved feature selection based on a mutual information measure for hyperspectral image classification. In Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International (pp. 3058-3061). IEEE.
    連結：
  26. 30. Hsu, P. H. (2007). Feature extraction of hyperspectral images using wavelet and matching pursuit. ISPRS Journal of Photogrammetry and Remote Sensing, 62(2), 78-92.
    連結：
  27. 32. Keuchel, J., Naumann, S., Heiler, M., & Siegmund, A. (2003). Automatic land cover analysis for Tenerife by supervised classification using remotely sensed data. Remote sensing of environment, 86(4), 530-541.
    連結：
  28. 33. Kim, M., Warner, T. A., Madden, M., & Atkinson, D. S. (2011). Multi-scale GEOBIA with very high spatial resolution digital aerial imagery: scale, texture and image objects. International Journal of Remote Sensing, 32(10), 2825-2850.
    連結：
  29. 34. Kurnar, D. (2011). Monitoring forest cover changes using remote sensing and GIS: a global prospective. Research Journal of Environmental Sciences, 5(2), 105.
    連結：
  30. 36. Martha, T. R., Kerle, N., Jetten, V., van Westen, C. J., & Kumar, K. V. (2010). Characterising spectral, spatial and morphometric properties of landslides for semi-automatic detection using object-oriented methods. Geomorphology,116(1), 24-36.
    連結：
  31. 37. Martha, T. R., Kerle, N., Van Westen, C. J., Jetten, V., & Kumar, K. V. (2011). Segment optimization and data-driven thresholding for knowledge-based landslide detection by object-based image analysis. Geoscience and Remote Sensing, IEEE Transactions on, 49(12), 4928-4943
    連結：
  32. 38. Matsuoka, M., & Yamazaki, F. (2005). Building damage mapping of the 2003 Bam, Iran, earthquake using Envisat/ASAR intensity imagery. Earthquake Spectra, 21(S1), 285-294.
    連結：
  33. 39. Santangelo, M., Marchesini, I., Bucci, F., Cardinali, M., Fiorucci, F., & Guzzetti, F. (2015). An approach to reduce mapping errors in the production of landslide inventory maps. Nat. Hazards Earth Syst. Sci, 15, 2111-2126.
    連結：
  34. 40. Sharpe, C. F. S. (1938). Landslides and related phenomena: a study of mass-movements of soil and rock. Pageant Books.
    連結：
  35. 43. Treitz, P. M., Howarth, P. J., & Soulis, E. D. (1996, May). Textural processing of multi-polarization SAR for agricultural crop classification. In Geoscience and Remote Sensing Symposium, 1996. IGARSS'96.'Remote Sensing for a Sustainable Future.', International (Vol. 4, pp. 1986-1988). IEEE.
    連結：
  36. 46. Tuia, D., Volpi, M., Copa, L., Kanevski, M., & Muñoz-Marí, J. (2011). A survey of active learning algorithms for supervised remote sensing image classification. Selected Topics in Signal Processing, IEEE Journal of, 5(3), 606-617.
    連結：
  37. 48. Wasowski, J., Bovenga, F., Nutricato, R., & Oscar, D. (2014). High resolution satellite multi-temporal interferometry for detecting and monitoring landslide and subsidence hazards. Sat, 2007(2.5/2.5), 3-1.
    連結：
  38. 49. Woodcock, C., & Harward, V. J. (1992). Nested-hierarchical scene models and image segmentation. International Journal of Remote Sensing, 13(16), 3167-3187.
    連結：
  39. 50. Zand, M., Doraisamy, S., Halin, A. A., & Mustaffa, M. R. (2015). Texture classification and discrimination for region-based image retrieval. Journal of Visual Communication and Image Representation, 26, 305-316.
    連結：
  40. 5. 林耿帆、邱彥瑋、張智昌、徐百輝 (2011) 以物件導向結合影像與光達點雲資料之地物分類。台灣地理資訊學會年會暨學術研討會論文集。
  41. 10. 莊雲翰 (2002) 結合影像區塊及知識庫分類之研究-以Ikonos衛星影像為例。碩士論文，國立中央大學土木工程學系。
  42. 17. 謝寶珊、林彥享、鄭錦桐、余勝雄、楊勳得、焦中輝、黃連通、李錫堤 (2006) 以不同尺度之遙測影像進行崩塌地判釋之精度探討-以大甲溪集水區為例，中國地球物理學會年會暨學術研討會，pp.79。
  43. 19. Baatz, M., & Schäpe, A. (2000). Multiresolution segmentation: an optimization approach for high quality multi-scale image segmentation. Angewandte Geographische Informationsverarbeitung XII, 58, 12-23.
  44. 31. Kawy, W. A., & Belal, A. A. (2011). GIS to assess the environmental sensitivity for desertification in soil adjacent to El-Manzala Lake, East of Nile Delta, Egypt. Am. Eurasian J. Agric. Environ. Sci, 10(5), 844-856.
  45. 35. Lillesand, T. M., Kiefer, R. W., & Chipman, J. W. (2000). Remote sensing and image interpretation. Wiley, NY. 724pp.
  46. 41. Soeters, R., & van Westen, C. J. (1996). Slope instability recognition, analysis and zonation. Landslides, investigation and mitigation. Transportation Research Board, National Research Council, Special Report, 247, 129-177.
  47. 42. Song, M., & Civco, D. L. (2002, April). A knowledge-based approach for reducing cloud and shadow. In Proc. of (pp. 22-26).
  48. 44. Trimble. (2014a) eCognition Developer 8.0.2 - Reference Book. Munchen, Germany: Trimble Germany GmbH.
  49. 45. Trimble. (2014b) eCognition Developer 8.0.2 – User Guide. Munchen, Germany: Trimble Germany GmbH.
  50. 47. Varnes, D. J. (1978). Slope movement types and processes. Transportation Research Board Special Report, (176).
print cancel