Abstract
Remote sensing images have been widely employed to analyze bodies of water and have become essential to studying their dynamics. While the use of indices based on the threshold segmentation technique is preferred, the search for methods that define water edge contour continues. The segmentation algorithm introduced in this study is based on Mean-Shift and Watershed methods. We propose a fusion classifier strategy which allows us to obtain results that are consistent with the segmentation process. The use of two or more segmentation processes has been shown to improve pattern recognition. It is important to implement a good data integration scheme. Preliminary results suggest that the approach reported herein can improve the definition of lake shorelines.
Chapter PDF
Similar content being viewed by others
References
Schistad-Solberg, A.: Contextual Data Fusion Applied to Forest Map Revision. IEEE Transaction on Geoscience and Remote Sensing 37(3), 1234–1243 (1999)
Jeon, B., Landgrebe, D.A.: Decision Fusion Approach for Multitemporal Classification. IEEE Transaction on Geoscience and Remote Sensing 37(3), 1227–1233 (1999)
Zhang, J.: Multi-source remote sensing data fusion: Status and trends. International Journal of Image and Data Fusion 1(1), 5–24 (2010)
Song, C., Huang, B., Ke, L., Richards, K.S.: Remote sensing of alpine lake water environment changes on the Tibetan Plateau and surroundings: A review. ISPRS Journal of Photogrammetry and Remote Sensing 92, 26–37 (2014)
Castanedo, F.: Fusión de Datos Distribuida en Redes de Sensores Visuales Utilizando Sistemas Multi-Agente. Tesis Doctoral. Departamento de Informatica, Universidad de Carlos III de Madrid, Escuela Politécnica Superior 41 (2010)
Tsymbal, A., Pechenizkiy, M., Cunninghama, P.: Diversity in search strategies for ensemble feature selection. Information Fusion 6, 83–98 (2005)
Lam, L., Suen, C.Y.: Application of Majority Voting to Pattern Recognition: An Analysis of Its Behavior and Performance. IEEE Transactions on Systems, Man, and Cybernetics—Part A: Systems and Humans 27(5), 553 (1997)
Jimenez, L.O., Moales-Morell, A., Creus, A.: Classification of Hyperdimensional Data Based on Feature and Decision Fusion Approaches Using Projection Pursuit, Majority Voting, and Neural Networks. IEEE Transaction on Geoscience and Remote Sensing 37(3), 1360–1366 (1999)
McFeeters, S.: The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing (17), 1425–1432 (1996)
Xu, H.: Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensin 27, 3025–3033 (2006)
Luo, J., Sheng, Y., Shen, Z., Li, J., Gao, L.: Automatic and high-precise extraction for water information from multispectral images with the step-by-step iterative transformation mechanism. J. Remote Sens 13, 604–615 (2009)
Bagli, S., Soille, P., Fermi, E.: Automatic delineation of shoreline and lake boundaries from Landsat satellite images. In: Proceedings of Initial ECOIMAGINE GI and GIS for Integrated Coastal Management, Seville, pp. 13–15 (2004)
Verpoorter, C., Kutser, T., Tranvik, L.: Automated mapping of water bodies using Landsat multispectral data. Limnol. Oceanogr. Methods 10, 1037–1050 (2012)
Lopez-Caloca, A.: Aplicaciones de fusión de Datos en datos geoespaciales: Caso de estudio fusión de clasificadores múltiples en el Lago de Chapala. GEOcibernética: i+g+s, Open Access, http://www.geocibernetica.org/journal/ (to be published)
Ji, L., Zhang, L., Wylie, B.: Analysis of Dynamic Thresholds for the Normalized Difference Water Index. Photogrammetric Engineering & Remote Sensing 75(11), 1307–1317 (2009)
Bai, J., Chen, X., Li, J., Yang, L., Fang, H.: Changes in the area of inland lakes in arid regions of central Asia during the past 30 years. Environ Monitorig Assess 178, 247–256 (2011)
López-Caloca, A.A., Tapia-Silva, F.O., Escalante-Ramírez, B.: Lake Chapala change detection using time series. Remote Sensing for Agriculture, Ecosystems, and Hydrology X. In: Neale, C.M.U., Owe, M., D’Urso, G. (eds.) Remote Sensing for Agriculture, Ecosystems, and Hydrology X. Proceedings of the SPIE, vol. 7104, article id. 710405, p. 11 (2008)
Lua, S., Ouyangab, N., Wua, B., Weic, Y., Tesemma, Z.: Lake water volume calculation with time series remote-sensing images. International Journal of Remote Sensing 34(22), 7962–7973 (2013)
Otsu, N.: A threshold selection method from grey-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)
Sonka, M., Fitzpatrick, J.M.: Handbook of Med. Ima., vol. 2. SPIE Press (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
López-Caloca, A.A. (2014). Data Fusion Approach for Employing Multiple Classifiers to Improve Lake Shoreline Analysis. In: Bayro-Corrochano, E., Hancock, E. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2014. Lecture Notes in Computer Science, vol 8827. Springer, Cham. https://doi.org/10.1007/978-3-319-12568-8_124
Download citation
DOI: https://doi.org/10.1007/978-3-319-12568-8_124
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12567-1
Online ISBN: 978-3-319-12568-8
eBook Packages: Computer ScienceComputer Science (R0)