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Conference Paper

Semi-supervised Hyperspectral Image Classification with Graphs

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons84330

Zhou,  D
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Bandos, T., Zhou, D., & Camps-Valls, G. (2006). Semi-supervised Hyperspectral Image Classification with Graphs. Proceedings of the IEEE International Conference on Geoscience and Remote Sensing (IGARSS 2006), 3883-3886.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-D097-5
Abstract
This paper presents a semi-supervised graph-based method for the classification of hyperspectral images. The method is designed to exploit the spatial/contextual information in the images through composite kernels. The proposed method produces smoother classifications with respect to the intrinsic structure collectively revealed by known labeled and unlabeled points. Good accuracy in high dimensional spaces and low number of labeled samples (ill-posed situations) are produced as compared to standard inductive support vector machines.