nach oben

Erschienen in:

2017 | OriginalPaper | Buchkapitel

2. A Supervised Object-Based Detection of Landslides and Man-Made Slopes Using Airborne Laser Scanning Data

verfasst von : Biswajeet Pradhan, Ali Alsaleh

Erschienen in: Laser Scanning Applications in Landslide Assessment

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

In recent years, airborne-derived products from light detection and ranging (LiDAR) measurements, such as high-resolution digital elevation models (DEMs), slope, curvature, shaded relief, and maps of landslides obtained from beneath dense vegetation, are becoming increasingly important for producing a detailed landslide inventory map

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Laser Scanning Systems in Landslide Studies

Nächstes Kapitel Optimized Rule Sets for Automatic Landslide Characteristic Detection in a Highly Vegetated Forests

Abe, S. (2005). Support vector machines for pattern classification (Vol. 53). Berlin: Springer.

Akcay, H. G., & Aksoy, S. (2008). Automatic detection of geospatial objects using multiple hierarchical segmentations. IEEE Transactions on Geoscience and Remote Sensing, 46(7), 2097–2111.CrossRef

Benz, U. C., Hofmann, P., Willhauck, G., Lingenfelder, I., & Heynen, M. (2004). Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS Journal of Photogrammetry and Remote Sensing, 58(3), 239–258.CrossRef

Bignel, F., & Snelling, G. (1977). The geochronology of the main range Batholith: Cameron highlands road and Gunong Bujang Melaka. Overseas Geol Miner Resour, 47, 3–35.

Bottou, L., & Lin, C.-J. (2007). Support vector machine solvers. Large Scale Kernel Machines, 301–320.

Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.CrossRef

Breiman, L. (2003). RF/tools: A class of two-eyed algorithms. Paper presented at the SIAM workshop.

Breiman, L., Friedman, J., Stone, C. J., & Olshen, R. A. (1984). Classification and regression trees. Boca Raton: CRC Press.

Caruana, R., & Niculescu-Mizil, A. (2006). An empirical comparison of supervised learning algorithms. Paper presented at the proceedings of the 23rd international conference on machine learning.

Chen, L. C., Teo, T.-A., Shao, Y.-C., Lai, Y.-C., & Rau, J.-Y. (2004). Fusion of LiDAR data and optical imagery for building modeling. International Archives of Photogrammetry and Remote Sensing, 35(B4), 732–737.

Chen, W., Li, X., Wang, Y., Chen, G., & Liu, S. (2014). Forested landslide detection using LiDAR data and the random forest algorithm: A case study of the Three Gorges, China. Remote Sensing of Environment, 152, 291–301.CrossRef

Chow, W., Zakaria, M., Ferdaus, A., & Nurzaidi, A. (2003). Geological terrain mapping. JMG unpublished report. JMG. SWP. GS, 16, 1–42.

Cobbing, E., Pitfield, P., Darbyshire, D., & Mallick, D. (1992). The granites of the SE Asian tin belt. British Geological Survey, Overseas Memoir No. 10: HMSO, London.

Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.

Definiens, A. (2007). Definiens developer 7 reference book (pp. 21–24). München: Definiens AG.

Duro, D. C., Franklin, S. E., & Dubé, M. G. (2012). Multi-scale object-based image analysis and feature selection of multi-sensor earth observation imagery using random forests. International Journal of Remote Sensing, 33(14), 4502–4526.CrossRef

Eeckhaut, M., Poesen, J., Verstraeten, G., Vanacker, V., Nyssen, J., Moeyersons, J., et al. (2007). Use of LiDAR-derived images for mapping old landslides under forest. Earth Surface Processes and Landforms, 32(5), 754–769.CrossRef

Fang, H.-T., & Huang, D.-S. (2004). Noise reduction in LiDAR signal based on discrete wavelet transform. Optics Communications, 233(1), 67–76.CrossRef

Foody, G. M. (2004). Thematic map comparison. Photogrammetric Engineering & Remote Sensing, 70(5), 627–633.CrossRef

Friedman, N., Geiger, D., & Goldszmidt, M. (1997). Bayesian network classifiers. Machine Learning, 29(2–3), 131–163.CrossRef

Galli, M., Ardizzone, F., Cardinali, M., Guzzetti, F., & Reichenbach, P. (2008). Comparing landslide inventory maps. Geomorphology, 94(3), 268–289.CrossRef

Gibril, M. B. A., Bakar, S. A., Yao, K., Idrees, M. O., & Pradhan, B. (2016). Fusion of RADARSAT-2 and multispectral optical remote sensing data for LULC extraction in a tropical agricultural area. Geocarto International, 1–14.

Gorum, T., Fan, X., van Westen, C. J., Huang, R. Q., Xu, Q., Tang, C., et al. (2011). Distribution pattern of earthquake-induced landslides triggered by the 12 May 2008 Wenchuan earthquake. Geomorphology, 133(3), 152–167.CrossRef

Grimm, R., Behrens, T., Märker, M., & Elsenbeer, H. (2008). Soil organic carbon concentrations and stocks on Barro Colorado Island—digital soil mapping using Random Forests analysis. Geoderma, 146(1), 102–113.CrossRef

Guo, L., Chehata, N., Mallet, C., & Boukir, S. (2011). Relevance of airborne lidar and multispectral image data for urban scene classification using Random Forests. ISPRS Journal of Photogrammetry and Remote Sensing, 66(1), 56–66.CrossRef

Hodgson, M. E., Jensen, J., Raber, G., Tullis, J., Davis, B. A., Thompson, G., et al. (2005). An evaluation of lidar-derived elevation and terrain slope in leaf-off conditions. Photogrammetric Engineering & Remote Sensing, 71(7), 817–823.CrossRef

Laliberte, A. S., Rango, A., Havstad, K. M., Paris, J. F., Beck, R. F., McNeely, R., et al. (2004). Object-oriented image analysis for mapping shrub encroachment from 1937 to 2003 in southern New Mexico. Remote Sensing of Environment, 93(1), 198–210.CrossRef

Last, M., Maimon, O., & Minkov, E. (2002). Improving stability of decision trees. International Journal of Pattern Recognition and Artificial Intelligence, 16(02), 145–159.CrossRef

Liaw, A., & Wiener, M. (2002). Classification and regression by random forest. R News, 2(3), 18–22.

Lillesand, T. M., Kiefer, R. W., & Chipman, J. (2004). Remote sensing and image interpretation. New York: Wiley.

Long, N. T. (2008). Landslide susceptibility mapping of the mountainous area in A Luoi district, Thua Thien Hue province, Vietnam. Faculty of Engineering, Department of Hydrology and Hydraulic Engineering, Vrije Universiteit Brussel, Belgium.

Martha, T. R. (2011). Detection of landslides by object oriented image analysis. University of Twente, Faculty of Geo-Information Science and Earth Observation. Enschede, The Netherlands: ITC Printing Department.

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. IEEE Transactions on Geoscience and Remote Sensing, 49(12), 4928–4943.CrossRef

McKean, J., & Roering, J. (2004). Objective landslide detection and surface morphology mapping using high-resolution airborne laser altimetry. Geomorphology, 57(3), 331–351.CrossRef

Mitchell, T. M. (1997). Machine learning (Vol. 45, p. 37). Burr Ridge, IL: McGraw Hill.

Möller, M., Lymburner, L., & Volk, M. (2007). The comparison index: A tool for assessing the accuracy of image segmentation. International Journal of Applied Earth Observation and Geoinformation, 9(3), 311–321.CrossRef

Mutanga, O., Adam, E., & Cho, M. A. (2012). High density biomass estimation for wetland vegetation using WorldView-2 imagery and random forest regression algorithm. International Journal of Applied Earth Observation and Geoinformation, 18, 399–406.CrossRef

Navulur, K. (2006). Multispectral image analysis using the object-oriented paradigm. Boca Rotan: CRC Press.CrossRef

Ohlmacher, G. C. (2007). Plan curvature and landslide probability in regions dominated by earth flows and earth slides. Engineering Geology, 91(2), 117–134.CrossRef

Olaya, V. (2009). Basic land-surface parameters. Developments in Soil Science, 33, 141–169.CrossRef

Pradhan, B., & Lee, S. (2010). Landslide susceptibility assessment and factor effect analysis: Backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environmental Modelling and Software, 25(6), 747–759.CrossRef

Puissant, A., Rougier, S., & Stumpf, A. (2014). Object-oriented mapping of urban trees using Random Forest classifiers. International Journal of Applied Earth Observation and Geoinformation, 26, 235–245.CrossRef

Rodriguez-Galiano, V. F., Ghimire, B., Rogan, J., Chica-Olmo, M., & Rigol-Sanchez, J. P. (2012). An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS Journal of Photogrammetry and Remote Sensing, 67, 93–104.CrossRef

Samui, P. (2008). Slope stability analysis: A support vector machine approach. Environmental Geology, 56(2), 255–267.CrossRef

Soria, D., Garibaldi, J. M., Ambrogi, F., Biganzoli, E. M., & Ellis, I. O. (2011). A ‘non-parametric’ version of the naive Bayes classifier. Knowledge-Based Systems, 24(6), 775–784.CrossRef

Team, R. C. (2013). R: A language and environment for statistical computing.

Vapnik, V. N., & Vapnik, V. (1998). Statistical learning theory (Vol. 1). New York: Wiley.

Varmuza, K., & Filzmoser, P. (2016). Introduction to multivariate statistical analysis in chemometrics. Boca Rotan: CRC Press.

Wu, X., Kumar, V., Quinlan, J. R., Ghosh, J., Yang, Q., Motoda, H., et al. (2008). Top 10 algorithms in data mining. Knowledge and Information Systems, 14(1), 1–37.CrossRef

Xie, Z., Zhang, Q., Hsu, W., & Lee, M. L. (2005). Enhancing SNNB with local accuracy estimation and ensemble techniques. Paper presented at the international conference on database systems for advanced applications.

Zêzere s, J. L., de Brum Ferreira, A., & Rodrigues, M Ls. (1999). The role of conditioning and triggering factors in the occurrence of landslides: A case study in the area north of Lisbon (Portugal). Geomorphology, 30(1), 133–146.CrossRef

Zhang, H. (2004). The optimality of naive Bayes. AA, 1(2), 3.

Titel: A Supervised Object-Based Detection of Landslides and Man-Made Slopes Using Airborne Laser Scanning Data
verfasst von: Biswajeet Pradhan
Ali Alsaleh
Verlag: Springer International Publishing
Buch: Laser Scanning Applications in Landslide Assessment
Print ISBN: 978-3-319-55341-2

Electronic ISBN: 978-3-319-55342-9

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-55342-9_2