Abstract
Multispectral remote sensing images have been regarded as dataset which contains incredible semantic information. And classifying multispectral remote sensing images could, in a sense, be achieved by analyzing a variety of complex semantic information and distilling skeletonzed information which facilitates the generalization, calculation and decision-making of human beings. However, conventional interpretation of remote sensing images is mostly limited within the extent of feature extraction and selection of merely spectral features of terrestrial objects. This paper present a Remote sensing Image Classification method based on SVM and Object Semantic, and it can obtain better performance of image classification.
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References
Furey, T.S., Cristianini, N., Duffy, N., Bednarski, D.W., Schummer, M., Haussler, D.: Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 16(10), 906–914 (2000)
He, L.M., Kong, F.S., Sheng, Z.Q.: Multiclass SVM based land cover classification with multisource data. In: Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, Guangzhou, China, pp. 3541–3545 (2005)
Huang, X., Zhang, L., Li, P.: Classification of High Spatial Resolution Remotely Sensed Imagery Based Upon Fusion of Multiscale Features and SVM. Journal of Remote Sensing 11(1), 48–54 (2007)
Hammer, B., Gersmann, K.: A note on the universal approximation capability of support vector machines. Neural Process Lett. 17(1), 43–45 (2003)
Kanellopoulos, I., Wilkinson, G.G.: Strategies and best p ractice for neural network image classification. International Journal of Remote Sensing 18(4), 711–725 (1997)
Cristianini, N., Shawe-Taylor, J.: An introduction to support vector machines: and other kernel-based learning methods. Cambridge Press, Cambridge (2000)
Kaya, G.T., Ersoy, O.K., Kamasak, M.E.: Hybrid SVM and SVSA method for classification of remote sensing images. In: IGARSS 2010 (2010)
Kaya, G.T., Ersoy, O.K., Kamasak, M.E.: Hybrid SVM and SVSA Method for Classification of Remote Sensing Images. In: International Geoscience and Remote Sensing Symposium (IGARSS), pp. 2828–2831 (2010)
Yue, S., Li, P., Hao, P.: Svm classification:its contents and challenges. Appl. Math. Chin. Univ. 18(3), 332–342 (2003)
Melgani, F., Bruzzone, L.: Classification of hyperspectral remote sensing images with support vector machines. IEEE Transactions on Geoscience and Remote Sensing 42(8), 1778–1790 (2004)
Tan, X., Bian, F.: Heterogeneous Spatial Information Interoperability Based on Cooperative Ontologies. Geomatics and Information Science of Wuhan University 30(2), 178–181 (2005)
Tan, X., Bian, F.: Heterogeneous Spatial Information System Semantic Interoperability Based on Bayes Data Classification and Ontology. Geomatics and Information Science of Wuhan University 31(8), 724–727 (2006)
Yi, R., Xu, F., Deng, M., Liu, Q.: An Approach for Hierarchical Semantic Classification of IslandBased on Formal Concept Analysis. Geomatics and Information Science of Wuhan University 37(8), 897–901 (2012)
Li, X.-M., Li, G.: The Meaning of Keywords GCMD in the Design of Geosciece Ontology. Remote Sensing Information (5), 92–95 (2008)
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Tan, X., Song, Y., Xiang, W. (2013). Remote Sensing Image Classification Based on SVM and Object Semantic. In: Bian, F., Xie, Y., Cui, X., Zeng, Y. (eds) Geo-Informatics in Resource Management and Sustainable Ecosystem. Communications in Computer and Information Science, vol 398. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45025-9_73
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DOI: https://doi.org/10.1007/978-3-642-45025-9_73
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-45024-2
Online ISBN: 978-3-642-45025-9
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