Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/68979
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dc.contributor.authorYuan, Nanqi
dc.date.accessioned2016-08-22T06:38:50Z
dc.date.available2016-08-22T06:38:50Z
dc.date.issued2016
dc.identifier.urihttp://hdl.handle.net/10356/68979
dc.description.abstractHyperspectral Image Classification is an important research problem in remote sensing.Classification is one of the most popular topic in hyperspectral remote sensing. In the last twenty years, a huge quantity of methods were proposed to deal with the hyperspectral data classification problem. Deep learning has been shown to be very promissing for this problem. However, existing deep learning methods only try to learn features from a pixel/region independently without considering the dependency between different pixels/regions.This project will employ Convolutional Neural Networks for learning features based on the spatial-spectral information of hyperspectral images. Experiments are conducted on benchmark datasets.en_US
dc.format.extent59 p.en_US
dc.language.isoenen_US
dc.subjectDRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processingen_US
dc.titleConvolutional neural network for hyperspectral image classificationen_US
dc.typeThesis
dc.contributor.supervisorWang Gangen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeMaster of Science (Signal Processing)en_US
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