Abstract
Remote sensing is an important tool in a variety of scientific researches which can help to study and solve many practical environmental problems. Classification of remote sensing image, however, is usually complex in many respects that a lot of different ground objects show mixture distributions in space and change with temporal variations. Therefore, automatic classification of land covers is of practical significance to the exploration of desired information. Recently, support vector machine (SVM) has shown its capability in solving multi-class classification for different ground objects. In this paper, the extension of SVM to its online version is employed for terrain image classification. An illustration of online SVM learning and classification on San Francisco Bay area is also presented to demonstrate its applicability.
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Chen, MS., Hwang, C., Ho, TY. (2013). Terrain Image Classification with SVM. In: Tan, Y., Shi, Y., Mo, H. (eds) Advances in Swarm Intelligence. ICSI 2013. Lecture Notes in Computer Science, vol 7929. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38715-9_11
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DOI: https://doi.org/10.1007/978-3-642-38715-9_11
Publisher Name: Springer, Berlin, Heidelberg
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