Convolutional neural network for hyperspectral image classification

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 l...

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Main Author: Yuan, Nanqi
Other Authors: Wang Gang
Format: Theses and Dissertations
Language:English
Published: 2016
Subjects:
Online Access:http://hdl.handle.net/10356/68979
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-689792023-07-04T15:05:09Z Convolutional neural network for hyperspectral image classification Yuan, Nanqi Wang Gang School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing 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. Master of Science (Signal Processing) 2016-08-22T06:38:50Z 2016-08-22T06:38:50Z 2016 Thesis http://hdl.handle.net/10356/68979 en 59 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing
spellingShingle DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing
Yuan, Nanqi
Convolutional neural network for hyperspectral image classification
description 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.
author2 Wang Gang
author_facet Wang Gang
Yuan, Nanqi
format Theses and Dissertations
author Yuan, Nanqi
author_sort Yuan, Nanqi
title Convolutional neural network for hyperspectral image classification
title_short Convolutional neural network for hyperspectral image classification
title_full Convolutional neural network for hyperspectral image classification
title_fullStr Convolutional neural network for hyperspectral image classification
title_full_unstemmed Convolutional neural network for hyperspectral image classification
title_sort convolutional neural network for hyperspectral image classification
publishDate 2016
url http://hdl.handle.net/10356/68979
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