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Algorithms and Applications for Land Cover Classification – A Review

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Geospatial Technology for Earth Observation

Abstract

During the last decades the manner how the Earth is being observed was revolutionized. Earth Observation (EO) systems became a valuable and powerful tool to monitor the Earth and had significant impact on the acquisition and analysis of environmental data (Rosenquist et al. 2003). Currently, EO data play a major role in supporting decision-making and surveying compliance of several multilateral environmental treaties, such as the Kyoto Protocol, the Convention on Biological Diversity, or the European initiative Global Monitoring for Environment and Security, GMES (Peter 2004, Rosenquist et al. 2003, Backhaus and Beule 2005).

However, the need for such long-term monitoring of the Earth’s surface requires the standardized and coordinated use of global EO data sets, which has led, e.g., to the international Global Earth Observation System of Systems (GEOSS) initiative as well as to the Global Climate Observation System (GCOS) implementation plan (GCOS 2004, GEO 2005). The evolving EO technologies together with the requirements and standards arising from their exploitation demand increasingly improving algorithms, especially in the field of land cover classification

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Authors and Affiliations

  1. Faculty of Electrical and Computer Engineering, University of Iceland, 107, Reykjavik, Iceland

    Björn Waske & Jón Atli Benediktsson

  2. School of Computer Science, Fudan University, Shanghai, P.R.China

    Mingmin Chi

  3. Geomatics Laboratory, Humboldt-Universität zu Berlin, 10099, Berlin, Germany

    Sebastian van der Linden

  4. Remote Sensing Laboratories, Universität Zürich, 8057, Zurich, Switzerland

    Benjamin Koetz

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  1. Björn Waske
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  2. Mingmin Chi
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  3. Jón Atli Benediktsson
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  4. Sebastian van der Linden
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  5. Benjamin Koetz
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Editors and Affiliations

  1. School of Remote Sensing, and Information Engineering, Wuhan University, 129 Luoyu Road, Wuhan, 430079, China, People's Republic

    Deren Li

  2. School of Civil Engineering, Purdue University, Stadium Mall Drive 550, West Lafayette, 47907-2051, Indiana, USA

    Jie Shan

  3. School of Remote Sensing, and Information Engineering, Wuhan University, 129 Luoyu Road, Wuhan, 430079, China, People's Republic

    Jianya Gong

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Waske, B., Chi, M., Atli Benediktsson, J., van der Linden, S., Koetz, B. (2010). Algorithms and Applications for Land Cover Classification – A Review. In: Li, D., Shan, J., Gong, J. (eds) Geospatial Technology for Earth Observation. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-0050-0_8

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