Flooding extent cartography with Landsat TM imagery and regularized kernel Fisher's discriminant analysis

Computers and Geosciences

Volumn 57, Issue , 2013, Pages 24-31

  Volpi, Michele ^a   Petropoulos, George P ^b   Kanevski, Mikhail ^a  

^a UNIVERSITY OF LAUSANNE   (Switzerland)

^b ABERYSTWYTH UNIVERSITY   (United Kingdom)

Author keywords

Change detection; Classification; Flood mapping; Kernel methods; Natural hazard; Remote sensing

Indexed keywords

CHANGE DETECTION; CLASSIFICATION PROCEDURE; FLOOD MAPPING; KERNEL METHODS; MULTI-SPECTRAL IMAGERY; NATURAL HAZARD; NONLINEAR CLASSIFIERS; STANDING WATERS;

CLASSIFICATION (OF INFORMATION); DISCRIMINANT ANALYSIS; IMAGE CLASSIFICATION; MAPPING; MAPS; REMOTE SENSING; SIGNAL DETECTION;

FLOODS;

CARTOGRAPHY; DISCRIMINANT ANALYSIS; FLOODING; IMAGE CLASSIFICATION; LANDSAT THEMATIC MAPPER; MAPPING; MULTISPECTRAL IMAGE; NATURAL HAZARD; REMOTE SENSING; SATELLITE IMAGERY;

SOUTH DAKOTA; UNITED STATES;

EID: 84877029549     PISSN: 00983004     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cageo.2013.03.009     Document Type: Article

References (45)

1. Aizerman, M.A., Braverman, E.A., Rozonoer, L., 1964. Theoretical foundations of the potential function method in pattern recognition learning. Automation and Remote Control 821-837.
2. Bachmann C.M., Ainsworth T.L., Fusina R.A. Exploiting manifold geometry in hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing 2005, 43:441-454.
3. Bandos T.V., Bruzzone L., Camps-Valls G. Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transactions on Geoscience and Remote Sensing 2009, 47:862-873.
4. Bruzzone L., Prieto D.F. Automatic analysis of the difference image for unsupervised change detection. IEEE Transactions on Geoscience and Remote Sensing 2000, 38:1171-1182.
5. Kernel Methods for Remote Sensing Data Analysis 2009, John Wiley & Sons. G. Camps-Valls, L. Bruzzone (Eds.).
6. Chormanski, J., Okruszko, T., Ignar, S., Batelaan, O., Rebel, K.T., J., W.M., 2011. Flood mapping with remote sensing and hydrochemistry: a new method to distinguish the origin of flood water during floods. Ecological Engineering 37, 1334-1349.
7. Currey D.T. Identifying flood water movement. Remote Sensing of Environment 1977, 6:51-61.
8. Dey, C., Jia, X., Fraser, D., Wang, L., 2009. Mixed pixels analysis for flood mapping using extended support vector machines. In: DICTA, Melbourne (AUS), pp. 291-295.
9. Fisher R.A. The use of multiple measurements in taxonomic problems. Annals of Eugenics 1936, 7:179-188.
10. Foody G.M. Thematic map comparison. evaluating the statistical significance of differences in classification accuracy. Photogrammetric Engineering & Remote Sensing 2004, 70:627-633.
11. Foody G.M., Mathur A. Toward intelligent training of supervised image classifications. directing training data acquisition for SVM classification. Remote Sensing of Environment 2004, 93:107-117.
12. Friedman J.H. Regularized discriminant analysis. Journal of the American Statistical Association 1989, 84:165-175.
13. Frohn D., Autrey B.C., Lane C.R., Reif M. Segmentation and object-oriented classification of wetlands in a karst Florida landscape using multi-season Landsat-7 ETM+ imagery. International Journal of Remote Sensing 2011, 32:1471-1489.
14. Gianinetto M., Villa P. Rapid response flood assessment using minimum noise fraction and composed spline interpolation. IEEE Transactions on Geoscience and Remote Sensing 2007, 45:3204-3211.
15. Gianinetto M., Villa P., Lechi G. Postflood damage evaluation using Landsat TM and ETM+ and data integrated with DEM. IEEE Transactions on Geoscience and Remote Sensing 2006, 44:236-243.
16. Hess L.L., Melack J.M., Filoso S., Wang Y. Delineation of inundated area and vegetation along the Amazon floodplain with the SIR-C synthetic aperture radar. IEEE Transactions on Geoscience and Remote Sensing 1995, 33:894-896.
17. Huang, S.Y., Hwang, C.R., 2006. Kernel fisher discriminant analysis in Gaussian reproducing kernel Hilbert spaces-theory. Technical Report. Institute of Statistical Sciences, Academia sinica, Taipei, China.
18. Hudson P.F., Colditz R.R. Flood delineation in a large and complex alluvial valley, lower Pánuco basin, Mexico. Journal of Hydrology 2003, 280:229-245.
19. Hughes G. On the mean accuracy of statistical pattern recognizers. IEEE Transactions on Information Theory 1968, 14:55-63.
20. Ip F., Dohm J.M., Baker V., Doggett T., Davies A.G., Castaño R., Chien S., Cichy B., Greeley R., Sherwood R.H., Tran D., Rabideau G. Flood detection and monitoring with the autonomous sciencecraft experiment onboard EO-1. Remote Sensing of Environment 2006, 101:463-481.
21. Kanevski M., Pozdnoukhov A., Timonin V. Machine Learning Algorithms for Spatial Environmental Data. Theory, Applications and Software 2009, EPFL Press, Lausanne.
22. Khan S.I., Hong Y., Wang J., Yilmaz K.K., Gourley J.J., Adler R.F., Brakenridge G.R., Policelli F., Habib S., Irwin D. Satellite remote sensing and hydrologic modeling for flood inundation mapping in lake victoria basin. implications for hydrologic prediction in ungauged basins. IEEE Transactions on Geoscience and Remote Sensing 2011, 49:85-95.
23. Longbotham N., Glenn T., Zare A., Volpi M., Tuia D., Cristophe E., Michel J., Inglada J., Chanussot J., Du Q., Pacifici F. Multi-modal change detection, application to the detection of flooded areas. outcome of the 2009-2010 data fusion contest. IEEE Journal of Selected Topics in Applied Earth Observations 2012, 5:1-12.
24. Martinis S., Twele A., Voigt S. Towards operational near real-time flood detection using a split-based automatic thresholding procedure on high resolution TerraSAR-X data. Natural Hazards and Earth System Sciences 2009, 9:303-314.
25. Mika, S., Rätsch, G., Müller, K.R., 2000. A mathematical programming approach to the kernel fisher algorithm. In: NIPS, Denver (USA), pp. 591-597.
26. Mika, S., Rätsch, G., Weston, J., Schölkopf, B., Müller, K.R., 1999. Fisher discriminant analysis with kernels. In: Proceedings of the IEEE Neural Networks Signal, Madison (USA), pp. 41-48.
27. de Morsier, F., Borgeaud, M., Küchler, C., Gass, V., Thiran, J.P., 2012. Semi-supervised and unsupervised novelty detection using nested support vector machine. In: IEEE IGARSS, Munich (D), pp. 7337-7340.
28. O'Grady D., Leblanc M., Gillieson D. Use of ENVISAT ASAR global monitoring mode to complement optical data in the mapping of rapid broad-scale flooding in Pakistan. Hydrology and Earth System Sciences 2011, 15:3475-3494.
29. Petropoulos G., Kontoes C., Keramitsoglou I. Burnt area delineation from a uni-temporal perspective based on Landsat TM imagery classification using support vector machines. International Journal of Applied Earth Observation 2011, 13:70-80.
30. Petropoulos G.P., Partsinevelos P., Mitraka Z. Change detection of surface mining activity and reclamation based on a machine learning approach of multi-temporal Landsat TM imagery. Geocarto International 2012, 1-20. , 10.1080/10106049.2012.706648.
31. Pulvirenti L., Chini M., Pierdicca N., Guerriero L., Ferrazzoli P. Flood monitoring using multi-temporal COSMO-SkyMed data. image segmentation and signature interpretation. Remote Sensing of Environment 2011, 115:990-1002.
32. Pulvirenti L., Pierdicca N., Chini M., Guerriero L. An algorithm for operational flood mapping from synthetic aperture radar (SAR) data using fuzzy logic. Natural Hazards and Earth System Sciences 2011, 11:529-540.
33. Radke R.J., Andra S., Al-Kofahi O., Roysam B. Image change detection algorithms. a systematic survey. IEEE Transactions on Image Processing 2005, 14:294-307.
34. Sanders R., Shaw F., MacKay H., Galy H., Foote M. National flood modelling for insurance purposes. using IFSAR for flood risk estimation in Europe. Hydrology and Earth System Sciences 2005, 9:449-456.
35. Schölkopf B., Smola A. Learning with Kernels 2002, MIT press, Cambridge (MA).
36. Shawe-Taylor J., Cristianini N. Kernel Methods for Pattern Analysis 2004, Cambridge University Press.
37. Singh A. Digital change detection techniques using remotely sensed data. International Journal of Remote Sensing 1989, 10(6):989-1003.
38. Sugiyama M. Dimensionality reduction of multimodal labeled data by local Fisher discriminant analysis. Journal of Machine Learning Research 2007, 8:1027-1061.
39. Taubenböck H., Wurm M., Netzband M., Zwenzner H., Roth A., Rahman A., Dech S. Flood risks in urbanized areas-multi-sensoral approaches using remotely sensed data for risk assessment. Natural Hazards and Earth System Sciences 2011, 11:431-444.
40. Tuia D., Volpi M., Copa L., Kanevski M., Muñoz-Marí J. A survey of active learning algorithms for supervised remote sensing image classification. IEEE Journal of Selected Topics in Signal Processing 2011, 5:606-617.
41. Volpi M., Tuia D., Camps-Valls G., Kanevski M. Unsupervised change detection with kernels. IEEE Transactions on Geoscience and Remote Sensing Letters 2012, 9:1026-1030.
42. Volpi M., Tuia D., Kanevski M. Memory-based cluster sampling for remote sensing image classification. IEEE Transactions on Geoscience and Remote Sensing 2012, 50:3016-3096.
43. Volpi M., Tuia D., Kanevski M., Bovolo F., Bruzzone L. Supervised change detection in VHR images using contextual information and support vector machines. International Journal of Applied Earth Observation 2013, 20:77-85.
44. Yamagata Y., Akiyama T. Flood damage analysis using multitemporal Landsat Thematic Mapper data. International Journal of Remote Sensing 1988, 9:503-514.
45. Zwenzner H., Voigt S. Improved estimation of flood parameters by combining space based SAR data with very high resolution digital elevation data. Hydrology and Earth System Sciences 2009, 13:567-576.