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Decision Fusion, Classification of Multisource Data

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  • First Online:
Encyclopedia of Remote Sensing

Part of the book series: Encyclopedia of Earth Sciences Series ((EESS))

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Definition

In the context of remote sensing–based land cover classification, many applications are aiming on the combination (i.e., fusion) of different data sources, for example, remote sensing images from different Earth Observation systems, in order to improve the classification accuracy. Decision fusion is a potential approach and is defined as the concept of combining information from different data sources, after each source has been classified individually.

Introduction

Over the past decades, numerous Earth Observation (EO) systems have been launched, providing various data sets. On one hand, passive instruments, such as Landsat-5 TM and Landsat-7 ETM+, the ASTER sensor on the Terra platform, Envisat’s MERIS, the SPOT satellites, and the AVNIR-2 on ALOS. These well-known optical EO systems are supplemented by passive instruments, such as ERS-2 SAR, Envisat ASAR, and Radarsat-1 as well as more recently launched systems such as TerraSAR-X, ALOS PALSAR, Cosmo-SkyMed, and the...

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Bibliography

  • Bachmann, C. M., Bettenhausen, M. H., Fusina, R. A., Donato, T. F., Russ, A. L., Burke, J. W., Lamela, G. M., Rhea, W. J., Truitt, B. R., and Porter, J. H., 2003. A credit assignment approach to fusing classifiers of multiseason hyperspectral imagery. IEEE Transaction on Geoscience and Remote Sensing, 41, 2488–2499.

    Google Scholar 

  • Benediktsson, J. A., and Kanellopoulos, I., 1999. Classification of multisource and hyperspectral data based on decision fusion. IEEE Transaction on Geoscience and Remote Sensing, 37, 1367–1377.

    Google Scholar 

  • Benediktsson, J. A., and Swain, P. H., 1992. Consensus theoretic classification methods. IEEE Transaction on Systems, Man and Cybernetic, 22, 688–704.

    Google Scholar 

  • Benediktsson, J. A., Swain, P. H., and Ersoy, O. K., 1990. Neural network approaches versus statistical-methods in classification of multisource remote-sensing data. IEEE Transaction on Geoscience and Remote Sensing, 28, 540–552.

    Google Scholar 

  • Benediktsson, J. A., Sveinsson, J. R., and Swain, P. H., 1997. Hybrid consensus theoretic classification. IEEE Transaction on Geoscience and Remote Sensing, 35, 833–843.

    Google Scholar 

  • Benediktsson, J. A., Pesaresi, M., and Arnason, K., 2003. Classification and feature extraction for remote sensing images from urban areas based on morphological transformations. IEEE Transaction on Geoscience and Remote Sensing, 41, 1940–1949.

    Google Scholar 

  • Blaes, X., Vanhalle, L., and Defourny, P., 2005. Efficiency of crop identification based on optical and SAR image time series. IEEE Transaction on Geoscience and Remote Sensing, 96, 352–365.

    Google Scholar 

  • Briem, G. J., Benediktsson, J. A., and Sveinsson, J. R., 2002. Multiple classifiers applied to multisource remote sensing data. IEEE Transaction on Geoscience and Remote Sensing, 40, 2291–2299.

    Google Scholar 

  • Brisco, B., and Brown, R. J., 1995. Multidate SAR/TM synergism for crop classification in western Canada. Photogrammetric Engineering and Remote Sensing, 61, 1009–1014.

    Google Scholar 

  • Bruzzone, L., and Carlin, L., 2006. A multilevel context-based system for classification of very high spatial resolution images. IEEE Transaction on Geoscience and Remote Sensing, 44, 2587–2600.

    Google Scholar 

  • Bruzzone, L., Chi, M., and Marconcini, M., 2006. A novel transductive SVM for semisupervised classification of remote-sensing images. IEEE Transaction on Geoscience and Remote Sensing, 44, 3363–3373.

    Google Scholar 

  • Camps-Valls, G., Gomez-Chova, L., Munoz-Mari, J., Rojo-Alvarez, J. L., and Martinez-Ramon, M., 2008. Kernel-based framework for multitemporal and multisource remote sensing data classification and change detection. IEEE Transaction on Geoscience and Remote Sensing, 46, 1822–1835.

    Google Scholar 

  • Cheung-Wai Chan, J., and Paelinckx, D., 2008. Evaluation of random forest and adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery. Remote Sensing of Environment, 112, 2999–3011.

    Google Scholar 

  • Chi, M., and Bruzzone, L., 2007. Semisupervised classification of hyperspectral images by SVMs optimized in the primal. IEEE Transaction on Geoscience and Remote Sensing, 45, 1870–1880.

    Google Scholar 

  • Chust, G., Ducrot, D., and Pretus, J. L., 2004. Land cover discrimination potential of radar multitemporal series and optical multispectral images in a Mediterranean cultural landscape. International Journal of Remote Sensing, 25, 3513–3528.

    Google Scholar 

  • Dalla Mura, M., Benediktsson, J. A., Waske, B., and Bruzzone, L., 2010. Extended profiles with morphological attribute filters for the analysis of hyperspectral data. International Journal of Remote Sensing, 31, 5975–5991.

    Google Scholar 

  • Dalponte, M., Bruzzone, L., and Gianelle, D., 2008. Fusion of hyperspectral and LIDAR remote sensing data for classification of complex forest areas. IEEE Transaction on Geoscience and Remote Sensing, 46, 1416–1427.

    Google Scholar 

  • De Wit, A. J. W., and Clevers, J. G. P. W., 2004. Efficiency and accuracy of per-field classification for operational crop mapping. International Journal of Remote Sensing, 68(11), 1155–1116.

    Google Scholar 

  • Duda, R. O., Hart, P. E., and Stork, D. G., 2001. Pattern Classification, 2nd edn. New York: Wiley-Interscience.

    Google Scholar 

  • Fauvel, M., Chanussot, J., and Benediktsson, J. A., 2006. Decision fusion for the classification of urban remote sensing images. IEEE Transaction on Geoscience and Remote Sensing, 44, 2828–2838.

    Google Scholar 

  • Fauvel, M., Benediktsson, J. A., Chanussot, J., and Sveinsson, J. R., 2008. Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles. IEEE Transactions on Geoscience and Remote Sensing, 46, 3804–3814.

    Google Scholar 

  • Fitzgerald, R. W., and Lees, B. G., 1994. Assessing the classification accuracy of multisource remote-sensing data. Remote Sensing of Environment, 47, 362–368.

    Google Scholar 

  • Foody, G. M., and Mathur, A., 2004. A relative evaluation of multiclass image classification by support vector machines. IEEE Transaction on Geoscience and Remote Sensing, 42, 1335–1343.

    Google Scholar 

  • Friedl, M. A., and Brodley, C. E., 1997. Decision tree classification of land cover from remotely sensed data. Remote Sensing of Environment, 61, 399–409.

    Google Scholar 

  • Hall, D. L., and Llinas, J., 1997. An introduction to multisensor data fusion. Proceedings of the IEEE, 85, 6–23.

    Google Scholar 

  • Huang, H., Legarsky, H., and Othman, M., 2007. Land-cover classification using Radarsat and Landsat imagery for St. Louis, Missouri. Photogrammetric Engineering and Remote Sensing, 73, 37–43.

    Google Scholar 

  • Jain, K., Duin, R. P. W., and Mao, J., 2000. Statistical pattern recognition: a review. IEEE Transaction on Pattern Analysis and Machine Intelligence, 22, 4–37.

    Google Scholar 

  • Jeon, B., Landgrebe, and D. A., 1999. Decision fusion approach for multitemporal classification. IEEE Transactions on Geoscience and Remote Sensing, 37(3), 1227–1233.

    Google Scholar 

  • Koetz, B., Morsdorf, F., van der Linden, S., Curt, T., and Allgöwer, B., 2008. Multi-source land cover classification for forest fire management based on imaging spectrometry and LiDAR data. Forest Ecology and Management, 256, 263–271.

    Google Scholar 

  • Kuncheva, L. I., 2004. Combining Pattern Classifiers. Methods and Algorithms. Hoboken, NJ: Wiley.

    Google Scholar 

  • Landgrebe, D., 2003. Signal Theory Methods in Multispectral Remote Sensing. New Jersey: Wiley.

    Google Scholar 

  • Michelson, D. B., Liljeberg, B. M., and Pilesjo, P., 2000. Comparison of algorithms for classifying Swedish landcover using LANDSAT TM and ERS-1 SAR data. Remote Sensing of Environment, 71, 1–15.

    Google Scholar 

  • Pedergnana, M., Marpu, P. R., Dalla Mura, M., Benediktsson, J. A., and Bruzzone, L., 2012. Classification of remote sensing optical and LiDAR data using extended attribute profiles. IEEE Journal of Selected Topics in Signal Processing, 6, 856–865.

    Google Scholar 

  • Pohl, C., and van Genderen, J. L., 1998. Multisensor image fusion in remote sensing: concepts, methods and applications. International Journal of Remote Sensing, 19, 823–854.

    Google Scholar 

  • Polikar, R., 2006. Ensemble based systems in decision making. IEEE Circuits and Systems Magazine, 6, 21–45.

    Google Scholar 

  • Richards, J. A., 2005. Analysis of remotely sensed data: the formative decades and the future. IEEE Transaction on Geoscience and Remote Sensing, 43, 422–432.

    Google Scholar 

  • Richards, J. A., and Jia, X., 2006. Remote Sensing Digital Image Analysis: An Introduction, 4th edn. New York: Springer.

    Google Scholar 

  • Roscher, R., Waske, B., and Förstner, W., 2012: Incremental import vector machines for classifying hyperspectral data. IEEE Transaction on Geoscience and Remote Sensing, 50, 1–11.

    Google Scholar 

  • Serpico, S. B., and Roli, F., 1995. Classification of multisensor remote-sensing images by structured neural networks. IEEE Transactions on Geoscience and Remote Sensing, 33(3), 562–578.

    Google Scholar 

  • Song, M., Civco, D. L., and Hurd, J. D., 2005. A competitive pixel-object approach for land cover classification. International Journal of Remote Sensing, 26, 4982–4997.

    Google Scholar 

  • Steele, B. M., 2000. Combining multiple classifiers: an application using spatial and remotely sensed information for land cover type mapping. Remote Sensing of Environment, 74, 545–556.

    Google Scholar 

  • van Genderen, J. L., and Pohl, C., 1994. Image fusion: issues, techniques and applications. Intelligent image fusion. In van Genderen, J. L., and Cappellini, V. (eds.), Proceedings EARSeL Workshop, September 11, 1994, Strasbourg, France. Enschede: ITC, pp. 18–26.

    Google Scholar 

  • Wald, L., 1999. Some terms of reference in data fusion. IEEE Transaction on Geoscience and Remote Sensing, 37, 1190–1193.

    Google Scholar 

  • Waske, B., and Benediktsson, J. A., 2014. Pattern recognition and classification. In Njoku, E. G. (ed.) Encyclopedia of Remote Sensing. New York: Springer.

    Google Scholar 

  • Waske, B., and Benediktsson, J. A., 2007. Fusion of support vector machines for classification of multisensor data. IEEE Transaction on Geoscience and Remote Sensing, 45, 3858–3866.

    Google Scholar 

  • Waske, B., and van der Linden, S., 2008. Classifying multilevel imagery from SAR and optical sensors by decision fusion. IEEE Transaction on Geoscience and Remote Sensing, 46, 1457–1466.

    Google Scholar 

  • Waske, B., van der Linden, S., Benediktsson, J. A., Rabe, A., and Hostert, P., 2010. Sensitivity of support vector machines to random feature selection in classification of hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, 48, 2880–2889.

    Google Scholar 

  • Wright, C., and Gallant, A., 2007. Improved wetland remote sensing in Yellowstone National Park using classification trees to combine TM imagery and ancillary environmental data. Remote Sensing of Environment, 107, 582–605.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Institute of Geodesy and Geoinformation, University of Bonn, Nussallee 15, 53115, Bonn, Ghana

    Björn Waske

  2. Faculty of Electrical and Computer Engineering, University of Iceland, Hjardarhagi 2-6, 107, Reykjavik, India

    Jón Atli Benediktsson

Authors
  1. Björn Waske
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  2. Jón Atli Benediktsson
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Corresponding author

Correspondence to Björn Waske .

Editor information

Editors and Affiliations

  1. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA

    Eni G. Njoku

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Waske, B., Benediktsson, J.A. (2014). Decision Fusion, Classification of Multisource Data. In: Njoku, E.G. (eds) Encyclopedia of Remote Sensing. Encyclopedia of Earth Sciences Series. Springer, New York, NY. https://doi.org/10.1007/978-0-387-36699-9_34

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