6533b7d9fe1ef96bd126c0eb

RESEARCH PRODUCT

Semi-supervised Hyperspectral Image Classification with Graphs

Dengyong ZhouT. BandosGustau Camps-valls

subject

Structured support vector machineContextual image classificationbusiness.industryHyperspectral imagingPattern recognitionGraphRelevance vector machineSupport vector machineComputingMethodologies_PATTERNRECOGNITIONKernel (image processing)Artificial intelligencebusinessCluster analysisMathematics

description

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 im- ages 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.

yearjournalcountryeditionlanguage
2006-07-012006 IEEE International Symposium on Geoscience and Remote Sensing
https://doi.org/10.1109/igarss.2006.996