Deep semantic understanding of high resolution remote sensing image

Qu, B; Li, X; Tao, D; Lu, X

Deep semantic understanding of high resolution remote sensing image

Qu, B Li, X Tao, D

Lu, X

Permalink

Publication Type:: Conference Proceeding
Citation:: IEEE CITS 2016 - 2016 International Conference on Computer, Information and Telecommunication Systems, 2016
Issue Date:: 2016-08-16

Closed Access

	Filename	Description	Size
	07546397.pdf	Published version	4.26 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Qu, B	en_US
dc.contributor.author	Li, X	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.contributor.author	Lu, X	en_US
dc.date.issued	2016-08-16	en_US
dc.identifier.citation	IEEE CITS 2016 - 2016 International Conference on Computer, Information and Telecommunication Systems, 2016	en_US
dc.identifier.isbn	9781509034406	en_US
dc.identifier.uri	http://hdl.handle.net/10453/102567
dc.description.abstract	© 2016 IEEE. With the rapid development of remote sensing technology, huge quantities of high resolution remote sensing images are available now. Understanding these images in semantic level is of great significance. Hence, a deep multimodal neural network model for semantic understanding of the high resolution remote sensing images is proposed in this paper, which uses both visual and textual information of the high resolution remote sensing images to generate natural sentences describing the given images. In the proposed model, the convolution neural network is utilized to extract the image feature, which is then combined with the text descriptions of the images by RNN or LSTMs. And in the experiments, two new remote sensing image-captions datasets are built at first. Then different kinds of CNNs with RNN or LSTMs are combined to find which is the best combination for caption generation. The experiments results prove that the proposed method achieves good performances in semantic understanding of high resolution remote sensing images.	en_US
dc.relation.ispartof	IEEE CITS 2016 - 2016 International Conference on Computer, Information and Telecommunication Systems	en_US
dc.relation.isbasedon	10.1109/CITS.2016.7546397	en_US
dc.title	Deep semantic understanding of high resolution remote sensing image	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2016 IEEE. With the rapid development of remote sensing technology, huge quantities of high resolution remote sensing images are available now. Understanding these images in semantic level is of great significance. Hence, a deep multimodal neural network model for semantic understanding of the high resolution remote sensing images is proposed in this paper, which uses both visual and textual information of the high resolution remote sensing images to generate natural sentences describing the given images. In the proposed model, the convolution neural network is utilized to extract the image feature, which is then combined with the text descriptions of the images by RNN or LSTMs. And in the experiments, two new remote sensing image-captions datasets are built at first. Then different kinds of CNNs with RNN or LSTMs are combined to find which is the best combination for caption generation. The experiments results prove that the proposed method achieves good performances in semantic understanding of high resolution remote sensing images.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/102567