Abstract
Land cover mapping is an important activity leading to the generation of various thematic products essential for numerous environmental monitoring and resources management applications at local, regional, and global levels. Over the years, various pattern recognition techniques have been developed to automate this process from remote sensor imagery. Support vector machines (SVM) as a group of relatively novel statistical learning algorithms have demonstrated their robustness in classifying homogeneous and heterogeneous land cover types. In this chapter, we review the status and potential challenges in the SVM implementation for land cover classification. The chapter is organized into two major parts. The first part reviews the research status of using SVM for land cover classification, focusing on some comparative studies that demonstrated the algorithm effectiveness over other conventional classifiers. We identify several areas for additional work, which are mostly related to appropriate treatments of some parametric and non-parametric factors in order to achieve improved mapping accuracies particularly for working over heterogeneous landscapes. Then, we implement the support vector machine technique to map various land cover types from a satellite image covering an urban area, and demonstrate the robustness of this pattern recognition technique for mapping heterogeneous landscapes.
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Acknowledgements
The authors like to thank the Florida State University for the time release in conducting this work. The research was partially supported by the Florida State University Council on Research and Creativity, CAS/SAFEA International Partnership Program for Creative Research Teams of “Ecosystem Processes and Services”, the Natural Science Foundation of China through the grant “A Study on Environmental Impacts of Urban Landscape Changes and Optimized Ecological Modeling” (ID 41230633).
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Shi, D., Yang, X. (2015). Support Vector Machines for Land Cover Mapping from Remote Sensor Imagery. In: Li, J., Yang, X. (eds) Monitoring and Modeling of Global Changes: A Geomatics Perspective. Springer Remote Sensing/Photogrammetry. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-9813-6_13
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DOI: https://doi.org/10.1007/978-94-017-9813-6_13
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