6533b827fe1ef96bd1286d91

RESEARCH PRODUCT

Semi-Supervised Remote Sensing Image Classification based on Clustering and the Mean Map Kernel

Luis Gómez-chovaLorenzo BruzzoneGustau Camps-vallsJavier Calpe-maravilla

subject

business.industryPattern recognitioncomputer.software_genreKernel principal component analysisComputingMethodologies_PATTERNRECOGNITIONKernel methodKernel embedding of distributionsPolynomial kernelVariable kernel density estimationKernel (statistics)Radial basis function kernelMean-shiftData miningArtificial intelligencebusinesscomputerMathematics

description

This paper presents a semi-supervised classifier based on the combination of the expectation-maximization (EM) algorithm for Gaussian mixture models (GMM) and the mean map kernel. The proposed method uses the most reliable samples in terms of maximum likelihood to compute a kernel function that accurately reflects the similarity between clusters in the kernel space. The proposed method improves classification accuracy in situations where the available labeled information does not properly describe the classes in the test image.

yearjournalcountryeditionlanguage
2008-01-01IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium
https://doi.org/10.1109/igarss.2008.4779740