RESEARCH ON THE NETWORK MAP SERVICE TECHNOLOGY OF REMOTE SENSING IMAGE INTELLIGENT CONVERSION BASED ON GAN MODEL

RESEARCH ON THE NETWORK MAP SERVICE TECHNOLOGY OF REMOTE SENSING IMAGE INTELLIGENT CONVERSION BASED ON GAN MODEL

Based on an improved generative adversarial networks algorithm (CGAN), this paper explores a technical way to realize map transformation through autonomous learning and training of remote sensing images. Just skip the trial process vector data update and cumbersome process of mapping the basic map e...

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Main Authors: D. Tang, W. Huang, Z. Zha, J. Yang, X. Zhou, C. Wang
Format: Article
Language:English
Published: Copernicus Publications 2021-06-01
Series:The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Online Access:https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B3-2021/285/2021/isprs-archives-XLIII-B3-2021-285-2021.pdf
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spelling doaj-697c4909e3174e1d92d8025e8b3cca2b2021-06-29T06:11:11ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342021-06-01XLIII-B3-202128529010.5194/isprs-archives-XLIII-B3-2021-285-2021RESEARCH ON THE NETWORK MAP SERVICE TECHNOLOGY OF REMOTE SENSING IMAGE INTELLIGENT CONVERSION BASED ON GAN MODELD. Tang0W. Huang1Z. Zha2J. Yang3X. Zhou4C. Wang5National Geomatics Center of China, 100830 Beijing, ChinaNational Geomatics Center of China, 100830 Beijing, ChinaNational Geomatics Center of China, 100830 Beijing, ChinaNational Geomatics Center of China, 100830 Beijing, ChinaBeijing Institute of Remote Sensing Information, 100085 Beijing, ChinaNational Geomatics Center of China, 100830 Beijing, ChinaBased on an improved generative adversarial networks algorithm (CGAN), this paper explores a technical way to realize map transformation through autonomous learning and training of remote sensing images. Just skip the trial process vector data update and cumbersome process of mapping the basic map elements can be automatically transform, the image on the main streets and typical rules of construction material, can achieve automatic identification and transformation, greatly shorten the tile map production and update cycle, improve the efficiency of the network map service quality. The results of the test platform have proved that it can be applied to a certain extent and can basically meet the requirements of network map production.https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B3-2021/285/2021/isprs-archives-XLIII-B3-2021-285-2021.pdf
collection DOAJ
language English
format Article
sources DOAJ
author D. Tang
W. Huang
Z. Zha
J. Yang
X. Zhou
C. Wang
spellingShingle D. Tang
W. Huang
Z. Zha
J. Yang
X. Zhou
C. Wang
RESEARCH ON THE NETWORK MAP SERVICE TECHNOLOGY OF REMOTE SENSING IMAGE INTELLIGENT CONVERSION BASED ON GAN MODEL
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
author_facet D. Tang
W. Huang
Z. Zha
J. Yang
X. Zhou
C. Wang
author_sort D. Tang
title RESEARCH ON THE NETWORK MAP SERVICE TECHNOLOGY OF REMOTE SENSING IMAGE INTELLIGENT CONVERSION BASED ON GAN MODEL
title_short RESEARCH ON THE NETWORK MAP SERVICE TECHNOLOGY OF REMOTE SENSING IMAGE INTELLIGENT CONVERSION BASED ON GAN MODEL
title_full RESEARCH ON THE NETWORK MAP SERVICE TECHNOLOGY OF REMOTE SENSING IMAGE INTELLIGENT CONVERSION BASED ON GAN MODEL
title_fullStr RESEARCH ON THE NETWORK MAP SERVICE TECHNOLOGY OF REMOTE SENSING IMAGE INTELLIGENT CONVERSION BASED ON GAN MODEL
title_full_unstemmed RESEARCH ON THE NETWORK MAP SERVICE TECHNOLOGY OF REMOTE SENSING IMAGE INTELLIGENT CONVERSION BASED ON GAN MODEL
title_sort research on the network map service technology of remote sensing image intelligent conversion based on gan model
publisher Copernicus Publications
series The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
issn 1682-1750
2194-9034
publishDate 2021-06-01
description Based on an improved generative adversarial networks algorithm (CGAN), this paper explores a technical way to realize map transformation through autonomous learning and training of remote sensing images. Just skip the trial process vector data update and cumbersome process of mapping the basic map elements can be automatically transform, the image on the main streets and typical rules of construction material, can achieve automatic identification and transformation, greatly shorten the tile map production and update cycle, improve the efficiency of the network map service quality. The results of the test platform have proved that it can be applied to a certain extent and can basically meet the requirements of network map production.
url https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B3-2021/285/2021/isprs-archives-XLIII-B3-2021-285-2021.pdf
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AT zzha researchonthenetworkmapservicetechnologyofremotesensingimageintelligentconversionbasedonganmodel
AT jyang researchonthenetworkmapservicetechnologyofremotesensingimageintelligentconversionbasedonganmodel
AT xzhou researchonthenetworkmapservicetechnologyofremotesensingimageintelligentconversionbasedonganmodel
AT cwang researchonthenetworkmapservicetechnologyofremotesensingimageintelligentconversionbasedonganmodel
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