Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/68979
Title: Convolutional neural network for hyperspectral image classification
Authors: Yuan, Nanqi
Keywords: DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing
Issue Date: 2016
Abstract: Hyperspectral 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.
URI: http://hdl.handle.net/10356/68979
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
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