Image fusion-based change detection for flood extent extraction using bi-temporal very high-resolution satellite images

Remote Sensing

Volumn 7, Issue 8, 2015, Pages 10347-10363

  Byun, Younggi ^a   Han, Youkyung ^b   Chae, Taebyeong ^c  

^a Information and Communications Core Technology Research Laboratory   (South Korea)

^b FONDAZIONE BRUNO KESSLER   (Italy)

^c Korea Aerospace Research Institute (KARI)   (South Korea)

Author keywords

Change detection; Flood extent; KOMPSAT 2 satellite imagery; Spectral distortion

Indexed keywords

ALGORITHMS; FLOOD CONTROL; FLOODS; IMAGE PROCESSING; MAXIMUM PRINCIPLE; SATELLITE IMAGERY; SATELLITES; SIGNAL DETECTION; SUPPORT VECTOR MACHINES;

CHANGE DETECTION; EXPECTATION-MAXIMIZATION ALGORITHMS; IMAGE QUALITY INDEX; SPECTRAL DISTORTIONS; SUPERVISED CHANGE DETECTION; UNSUPERVISED CHANGE DETECTION; VERY HIGH RESOLUTION; VERY HIGH RESOLUTION SATELLITE IMAGES;

IMAGE FUSION;

EID: 84939436339     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs70810347     Document Type: Article

References (38)

1. Hallegatte, S.; Green, C.; Nicholls, R.J.; Corfee-Morlot, J. Future flood losses in major coastal cities. Nat. Clim. Chang. 2013, 3, 802-806.
2. Peter, L.; Tatjana, V.; Matjaz, M.; Kristof, O. Detecting flooded areas with machine learning techniques: Case study of the Selska Sora river flash flood in Septemper 2007. Appl. J. Remote Sens. 2013, 7, 073564.
3. Sanyal, J.; Lu, X.X. Application of remote sensing in flood management with special reference to Monsoon Asia: A review. Nat. Hazards 2004, 33, 283-301.
4. Haq, M.; Akhtar, M.; Muhammad, S.; Paras, S.; Rahmatullah, J. Techniques of remote sensing and GIS for flood monitoring and damage assessment: A case study of Sindh province, Pakistan. Egypt. J. Remote Sens. Sp. Sci. 2012, 15, 135-141.
5. Schnebele, E.; Cervone, G. Improving remote sensing flood assessment using volunteered geographical data. Nat. Hazards Earth Syst. Sci. 2013, 13, 669-677.
6. Ticehurst, C.; Guerschman, J.P.; Chen, Y. The strengths and limitations in using the daily MODIS open water likelihood algorithm for identifying flood events. Remote Sens. 2014, 6, 11791-11809.
7. Jung, Y.; Kim, D.; Kim, D.; Kim, M.; Lee, S.O. Simplified flood inundation mapping based on flood elevation-discharge rating curves using satellite images in gauged watersheds. Water 2014, 6, 1280-1299.
8. Dewan, A.M.; Islam, M.M.; Kumamoto, T.; Nishigaki, M. Evaluating flood hazard for land-use planning in Greater Dhaka of Bangladesh using remote sensing and GIS techniques. Water Resour. Manag. 2007, 21, 1601-1612.
9. Martinez, J.M.; Toan, T.L. Mapping of flood dynamics and spatial distribution of vegetation in the Amazon floodplain using multitemporal SAR data. Remote Sens. Environ. 2007, 108, 209-223.
10. Kuenzer, C.; Guo, H.; Schlegel, I.; Tuan, V.Q.; Li, X.; Dech, S. Varying scale and capability of Envisat ASAR-WSM, TerraSAR-X Scansar and TerraSAR-X Stripmap data to assess urban flood situations: A case study of the Mekong Delta in Can Tho province. Remote Sens. 2013, 5, 5122-5142.
11. Wierzbicki, G.; Ostrowski, P.; Mazgajski, M.; Bujakowski, F. Using VHR multispectral remote sensing and LIDAR data to determine the geomorphological effects of overbank flow on a floodplain (the Vistula River, Poland). Geomorphology 2013, 183, 73-81.
12. Hussain, M.; Chen, D.; Cheng, A.; Wei, H.; Stanly, D. Change detection from remotely sensed image: From pixel-based to object-based approaches. ISPRS J. Photogramm. Remote Sens. 2013, 80, 91-106.
13. Nemmour, H.; Chibani, Y. Multiple support vector machines for land cover change detection: An application for mapping urban extension. ISPRS J. Photogramm. Remote Sens. 2006, 61, 125-133.
14. Chen, J.; Chen, X.; Cui, X. Change vector analysis in posterior probability space: A new method for land cover change detection. IEEE Geosci. Remote Sens. Lett. 2011, 8, 317-321.
15. Renza, D.; Martinez, E.; Arquero, A. A new approach to change detection in multispectral images by means of ERGAS index. IEEE Geosci. Remote Sens. Lett. 2013, 10, 76-80.
16. Canty, M.J.; Nielsen, A.A. Automatic radiometric normalization of multitemporal satellite imagery with the iteratively re-weighted MAD transformation. Remote Sens. Environ. 2008, 112, 1025-1036.
17. Marpu, P.R.; Gamba, P.; Canty, M.J. Improving change detection results of IR-MAD by eliminating strong changes. IEEE Geosci. Remote Sens. Lett. 2011, 8, 799-803.
18. Canty, M.J.; Nielsen, A.A. Linear and kernel methods for multivariate change detection. Comput. Geosci. 2012, 38, 107-114.
19. Dhakal, A.S.; Amada, T.; Aniya, M.; Sharma, R.R. Detection of areas associated with flood and erosion caused by a heavy rainfall using multispectral Landsat TM data. Photogramm. Eng. Remote Sens. 2002, 68, 233-239.
20. Nielsen, A.A.; Conradsen, K.; Simpson, J.J. Multivariate alteration detection (MAD) and MAF processing in multispectral, bitemporal image data: New approches to change detection studies. Remote Sens. Environ. 1998, 64, 1-19.
21. Longbotham, N.; Pacifici, F.; Glenn, T.; Zare, A.; Volpi, M.; Tuia, D.; Christophe, E.; Michel, J.; Inglada, J.; Chanussot, J.; et al. Multi-modal change detection, application to the detection of flooded areas: Outcome of the 2009-2010 data fusion contest. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 2012, 5, 331-342.
22. Giustraini, L.; Hostache, R.; Matgen, P.; Schumann, G.J.P.; Bates, P.D.; Mason, D.C. A change detection approach to flood mapping in urban areas using TerraSAR-X. IEEE Geosci. Remote Sens. 2013, 51, 1301-1312.
23. Long, S.; Fatoyinbo, T.E.; Policelli, F. Flood extent mapping for Namibia using change detection and thresholding with SAR. Enviorn. Res. Lett. 2014, 9, 35002-35011.
24. Almutairi, A.; Warner, T.A. Change detection accuracy and image properties: A study using simulated data. Remote Sens. 2010, 2, 1508-1529.
25. Bruzzone, L.; Cossu, R. An adaptive approach to reducing registration noise effects in unsupervised change detection. IEEE Trans. Geosci. Remote Sens. 2003, 41, 2455-2465.
26. Zhang, Y. Understanding image fusion. Photogramm. Eng. Remote Sens. 2004, 70, 657-661.
27. Witharana, C.; Civco, D.L.; Meyer, T. Evaluation of pansharpening algorithms in support of earth observation based rapid mapping workflow. Appl. Geogr. 2013, 37, 63-87.
28. Dou, W.; Chen, Y.; Li, X.; Sui, D.Z. A general framework for component substitution image fusion: An implementation using the fast image fusion method. Comput. Geosci. 2007, 33, 219-228.
29. Aiazzi, B.; Baronti, S.; Selva, M. Improving component substitution pansharpening through multivariate regression of MS+pan data. IEEE Geosci. Remote Sens. 2007, 45, 3230-3239.
30. Janzen, D.T.; Fredeen, A.L.; Wheate, R.D. Radiometric correction techniques and accuracy assessment for Landsat TM data in remote forested regions. Can. J. Remote Sens. 2006, 32, 330-340.
31. Thomas, C.; Ranchin, T.; Wald, L.; Chanussot, J. Synthesis of multispectral images to high spatial resolution: A critical review of fusion methods based on remote sensing physics. IEEE Geosci. Remote Sens. 2008, 46, 1301-1312.
32. Wang, Z.; Bovik, A.C. A uinversal image quality index. IEEE Signal Process. Lett. 2002, 9, 81-84.
33. Verbesselt, J.; Hyndman, R.; Zeileis, A.; Culvenor, D. Phenological change detection while accounting for abrupt and gradual trends in satellite image time series. Remote Sens. Environ. 2010, 114, 2970-2980.
34. Moon, T.K. The expectation-maximization problem. Signal Process. Mag. 1996, 13, 47-60.
35. Roberts, S.; Husmeier, D.; Rezek, I.; Penny, W. Bayesian approaches to gaussian mixture modeling. IEEE Pattern Anal. Mach. 1998, 20, 1133-1142.
36. Otsu, N. A threshold selection method from gray-level histogram. IEEE Syst. Man Cyhern. 1979, 9, 62-66.
37. Congalton, R.G. A review of assessing the accuracy of classification of remotely sensed data. Remote Sens. Environ. 1991, 37, 35-46.
38. Canty, M.J.; Nielsen, A.A. Visualization and unsupervised classification of changes in multispectral satellite imagery. Int. J. Remote Sens. 2006, 27, 3961-3975.