Automatic case-based reasoning approach for landslide detection: Integration of object-oriented image analysis and a genetic algorithm

Remote Sensing

Volumn 7, Issue 4, 2015, Pages 4318-4342

  Dou, Jie ^a   Chang, Kuan Tsung ^b   Chen, Shuisen ^c   Yunus, Ali P ^a   Liu, Jin King ^d   Xia, Huan ^e   Zhu, Zhongfan ^f  

^a UNIVERSITY OF TOKYO   (Japan)

^b MINGHSIN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Taiwan)

^c GUANGZHOU INSTITUTE OF GEOGRAPHY   (China)

^d LIDAR Technology Co Ltd   (Taiwan)

^e GUIZHOU UNIVERSITY OF FINANCE AND ECONOMICS   (China)

^f BEIJING NORMAL UNIVERSITY   (China)

Author keywords

Genetic algorithm (GA); Image classification; Image segmentation; Landslide detection; Object oriented image analysis (OOIA)

Indexed keywords

ALGORITHMS; CASE BASED REASONING; GENETIC ALGORITHMS; IMAGE ANALYSIS; IMAGE PROCESSING; IMAGE SEGMENTATION; LANDSLIDES; MAXIMUM LIKELIHOOD; MAXIMUM LIKELIHOOD ESTIMATION;

CASE-BASED REASONING APPROACHES; CASEBASED REASONINGS (CBR); CLASSIFICATION PERFORMANCE; FEATURE OPTIMIZATIONS; LANDSLIDE DETECTION; LANDSLIDE INVENTORIES; MAXIMUM LIKELIHOOD CLASSIFIERS; OBJECT-ORIENTED IMAGE ANALYSIS;

IMAGE CLASSIFICATION;

EID: 84937898139     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs70404318     Document Type: Article

References (47)

1. Turner, A.K.; Schuster, R. Landslides: Investigation and Mitigation; Transportation Research Board: Washington, DC, USA, 1996.
2. Alexander, D.E. A brief survey of GIS in mass-movement studies, with reflections on theory and methods. Geomorphology 2008, 94, 261-267.
3. García-Rodríguez, M.J.; Malpica, J.A.; Benito, B.; Díaz, M. Susceptibility assessment of earthquake-triggered landslides in El Salvador using logistic regression. Geomorphology 2008, 95, 172-191.
4. Guzzetti, F.; Mondini, A.C.; Cardinali, M.; Fiorucci, F.; Santangelo, M.; Chang, K.-T. Landslide inventory maps: New tools for an old problem. Earth-Sci. Rev. 2012, 112, 42-66.
5. Wang, L.-J.; Sawada, K.; Moriguchi, S. Landslide-susceptibility analysis using light detection and ranging-derived digital elevation models and logistic regression models: A case study in Mizunami City, Japan. J. Appl. Remote Sens. 2013, 7, 073561.
6. Assilzadeh, H.; Levy, J.K.; Wang, X. Landslide catastrophes and disaster risk reduction: A GIS framework for landslide prevention and management. Remote Sens. 2010, 2, 2259-2273.
7. Dou, J.; Oguchi, T.; Hayakawa, Y.S.Y.; Uchiyama, S.; Saito, H.; Paudel, U. GIS-based landslide susceptibility mapping using a certainty factor model and its validation in the Chuetsu Area, Central Japan. In Landslide Science for a Safer Geoenvironment; Springer International Publishing: Berlin, Germany, 2014; pp. 419-424.
8. Wu, Q.; Zhang, H.; Chen, F.; Dou, J. A web-based spatial decision support system for spatial planning and governance in the Guangdong Province. In Proceedings of the 2008 Geoinformatics and Joint Conference on GIS and Built Environment, Guangzhou, China, 28-29June 2008; Volume 7144, p. 12.
9. Zhou, S.; Fang, L. Support vector machine modeling of earthquake-induced landslides susceptibility in central part of Sichuan province, China. Geoenviron. Disasters 2015, 2, 1-12.
10. Lu, P.; Stumpf, A.; Kerle, N.; Casagli, N. Object-oriented change detection for landslide rapid mapping. IEEE Geosci. Remote Sens. Lett. 2011, 8, 701-705.
11. McKean, J.; Roering, J. Objective landslide detection and surface morphology mapping using high-resolution airborne laser altimetry. Geomorphology 2004, 57, 331-351.
12. Dou, J.; Qian, J.; Zhang, H.; Chen, S.; Zheng, X.; Zhu, J.; Xie, Z.; Zou, Y. Landslides detection: A case study in Conghua city of Pearl River delta. In Proceedings of the Second International Conference on Earth Observation for Global Changes, Chengdu, China, 25-29 May 2009; p. 11.
13. Myint, S.W.; Gober, P.; Brazel, A.; Grossman-Clarke, S.; Weng, Q. Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sens. Environ. 2011, 115, 1145-1161.
14. Li, X.; Yeh, A.G. Multitemporal SAR images for monitoring cultivation systems using case-based reasoning. Remote Sens. Environ. 2004, 90, 524-534.
15. Dou, J.; Zheng, X.; Qian, J.; Liu, R.; Wu, Q. Intelligence based automatic detection and classification of ground collapses using object-based image analysis method: A case study in Paitan of Pearl River delta. Proc. SPIE. 2008, doi:10.1117/12.813168.
16. Dou, J.; Zheng, X.; Qian, J.J.; Liu, R.; Wu, Q.; Chen, S.; Zhen, X. Object-based and case-based reasoning method for ground collapses detection. J. Image Graph. 2010, 15, 900-910.
17. Anders, N.S.; Seijmonsbergen, A.C.; Bouten, W. Segmentation optimization and stratified object-based analysis for semi-automated geomorphological mapping. Remote Sens. Environ. 2011, 115, 2976-2985.
18. Chang, K.-T.; Liu, J.; Wang, C. An object-oriented analysis for characterizing the rainfall-induced shallow landslide. J. Mar. Sci. Technol. 2012, 20, 647-656.
19. Qian, J.; Liu, R.; Chen, S.; Dou, J. Man-made ground collapse detection using high resolution Aerial image and object-based classification: Example of Pearl River Delta. In Proceedings of the Second International ISCRAM Workshop on Information Systems for Crisis Response and Management, Harbin, China, 26-27 August 2007; pp. 1-6.
20. Martha, T.R.; Kerle, N.; Jetten, V.; van Westen, C.J.; Kumar, K.V. Characterising spectral, spatial and morphometric properties of landslides for semi-automatic detection using object-oriented methods. Geomorphology 2010, 116, 24-36.
21. Yang, M. A genetic algorithm (GA) based automated classifier for remote sensing imagery. Can. J. Remote Sens. 2007, 33, 203-213.
22. Vancoillie, F.; Verbeke, L.; Dewulf, R. Feature selection by genetic algorithms in object-based classification of IKONOS imagery for forest mapping in Flanders, Belgium. Remote Sens. Environ. 2007, 110, 476-487.
23. Minor, M.; Bergmann, R.; Görg, S. Case-based adaptation of workflows. Inf. Syst. 2014, 40, 142-152.
24. Ahn, H.; Kim, K.; Han, I. Hybrid genetic algorithms and case-based reasoning systems for customer classification. Expert Syst. 2006, 23, 127-144.
25. Du, Y.; Wu, D.; Liang, F.; Li, C. GIScience & Remote Sensing integration of case-based reasoning and object-based image classification to classify SPOT images: A case study of aquaculture land use mapping in coastal areas of Guangdong province, China. GISci. Remote Sens. 2013, 50, 574-589.
26. Varnes, D.J. Slope movement types and processes. Natl. Acad. Sci. Transp. Res. Board Spec. Rep. 1978, 176, 12-33.
27. Baatz, M.; Benz, U.; Dehghani, S.; Heynen, M. ECognition User Guide_4.0; Definiens Imagine GmbH: Munich, Germany, 2004.
28. Benz, U.C.; Hofmann, P.; Willhauck, G.; Lingenfelder, I.; Heynen, M. Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS J. Photogramm. Remote Sens. 2004, 58, 239-258.
29. Dragut, L.; Tiede, D.; Levick, S.R. ESP: A tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data. Int. J. Geogr. Inf. Sci. 2010, 24, 859-871.
30. Goldberg, D.E. Genetic Algorithms in Search, Optimization, and Machine Learning; Addison-Wesley: Boston, MA, USA, 1989; p. 432.
31. Tang, A.M.; Quek, C.; Ng, G.S. GA-TSKfnn: Parameters tuning of fuzzy neural network using genetic algorithms. Expert Syst. Appl. 2005, 29, 769-781.
32. Iovine, G.; D'Ambrosio, D.; Di Gregorio, S. Applying genetic algorithms for calibrating a hexagonal cellular automata model for the simulation of debris flows characterised by strong inertial effects. Geomorphology 2005, 66, 287-303.
33. Kudo, M.; Sklansky, J. Comparison of algorithms that select features for pattern classifiers. Pattern Recognit. 2000, 33, 25-41.
34. Aamodt, A.; Plaza, E. Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI Commun. 1994, 7, 39-59.
35. Jonassen, D.H.; Hernandez-Serrano, J. Case-based reasoning and instructional design: Using stories to support problem solving. Educ. Technol. Res. Dev. 2002, 50, 65-77.
36. Deng, J.-L. Control problems of grey systems. Syst. Control Lett. 1982, 1, 288-294.
37. Matlab and Statistics Toolbox, Computer software. The MathWorks Inc.: Natick, MA, USA, 2010.
38. Radoux, J.; Bogaert, P. Accounting for the area of polygon sampling units for the prediction of primary accuracy assessment indices. Remote Sens. Environ. 2014, 142, 9-19.
39. Congalton, R.G. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens. Environ. 1991, 37, 35-46.
40. Yang, W.; Wang, M.; Shi, P. Using MODIS NDVI time series to identify geographic patterns of landslides in vegetated regions. IEEE Geosci. Remote Sens. Lett. 2013, 10, 707-710.
41. Pontius, R.G.; Millones, M. Death to Kappa: Birth of quantity disagreement and allocation disagreement for accuracy assessment. Int. J. Remote Sens. 2011, 32, 4407-4429.
42. Mondini, A.C.C.; Guzzetti, F.; Reichenbach, P.; Rossi, M.; Cardinali, M.; Ardizzone, F. Semi-automatic recognition and mapping of rainfall induced shallow landslides using optical satellite images. Remote Sens. Environ. 2011, 115, 1743-1757.
43. Swets, J.A. Measuring the accuracy of diagnostic systems. Science 1988, 240, 1285-1293.
44. Fawcett, T. An introduction to ROC analysis. Pattern Recognit. Lett. 2006, 27, 861-874.
45. Van den Eeckhaut, M.; Marre, A.; Poesen, J. Comparison of two landslide susceptibility assessments in the Champagne-Ardenne region (France). Geomorphology 2010, 115, 141-155.
46. Nichol, J.; Wong, M.S. Satellite remote sensing for detailed landslide inventories using change detection and image fusion. Int. J. Remote Sens. 2005, 26, 1913-1926.
47. Blaschke, T.; Feizizadeh, B.; Daniel, H. Object-based image analysis and digital terrain analysis for locating landslides in the Urmia Lake Basin, Iran. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2014, 7, 1-12.