MARTA GANs: Unsupervised Representation Learning for Remote Sensing Image Classification

With the development of deep learning, supervised learning has frequently been adopted to classify remotely sensed images using convolutional networks (CNNs). However, due to the limited amount of labeled data available, supervised learning is often difficult to carry out. Therefore, we proposed an...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:arXiv.org 2017-11
Hauptverfasser: Lin, Daoyu, Fu, Kun, Wang, Yang, Xu, Guangluan, Sun, Xian
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page
container_title arXiv.org
container_volume
creator Lin, Daoyu
Fu, Kun
Wang, Yang
Xu, Guangluan
Sun, Xian
description With the development of deep learning, supervised learning has frequently been adopted to classify remotely sensed images using convolutional networks (CNNs). However, due to the limited amount of labeled data available, supervised learning is often difficult to carry out. Therefore, we proposed an unsupervised model called multiple-layer feature-matching generative adversarial networks (MARTA GANs) to learn a representation using only unlabeled data. MARTA GANs consists of both a generative model \(G\) and a discriminative model \(D\). We treat \(D\) as a feature extractor. To fit the complex properties of remote sensing data, we use a fusion layer to merge the mid-level and global features. \(G\) can produce numerous images that are similar to the training data; therefore, \(D\) can learn better representations of remotely sensed images using the training data provided by \(G\). The classification results on two widely used remote sensing image databases show that the proposed method significantly improves the classification performance compared with other state-of-the-art methods.
doi_str_mv 10.48550/arxiv.1612.08879
format Article
fullrecord <record><control><sourceid>proquest_arxiv</sourceid><recordid>TN_cdi_arxiv_primary_1612_08879</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2076966733</sourcerecordid><originalsourceid>FETCH-LOGICAL-a523-f8185ea74fe56f0c7be54cad4be8d4a0ce788e0062069de9c192c9a5cb7d7f9e3</originalsourceid><addsrcrecordid>eNotj8tKAzEUhoMgWGofwJUB11MzyeTmbijaFqpCreBuSDMnJaXNjMlM0be3F1c__JfD-RC6y8m4UJyTRxN__GGci5yOiVJSX6EBZSzPVEHpDRqltCWEUCEp52yAvl7L5arE0_ItPeHPkPoW4sEnqPES2ggJQmc63wS8ABODDxvsmnjM9k0H-ANCOlnzvdkAnuxMSt55ex7comtndglG_zpEq5fn1WSWLd6n80m5yAynLHMqVxyMLBxw4YiVa-CFNXWxBlUXhliQSgEhghKha9A219Rqw-1a1tJpYEN0fzl7xq7a6Pcm_lYn_OqMf2w8XBptbL57SF21bfoYjj9VlEihhZCMsT-lKF4n</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2076966733</pqid></control><display><type>article</type><title>MARTA GANs: Unsupervised Representation Learning for Remote Sensing Image Classification</title><source>Freely Accessible Journals</source><source>arXiv.org</source><creator>Lin, Daoyu ; Fu, Kun ; Wang, Yang ; Xu, Guangluan ; Sun, Xian</creator><creatorcontrib>Lin, Daoyu ; Fu, Kun ; Wang, Yang ; Xu, Guangluan ; Sun, Xian</creatorcontrib><description>With the development of deep learning, supervised learning has frequently been adopted to classify remotely sensed images using convolutional networks (CNNs). However, due to the limited amount of labeled data available, supervised learning is often difficult to carry out. Therefore, we proposed an unsupervised model called multiple-layer feature-matching generative adversarial networks (MARTA GANs) to learn a representation using only unlabeled data. MARTA GANs consists of both a generative model \(G\) and a discriminative model \(D\). We treat \(D\) as a feature extractor. To fit the complex properties of remote sensing data, we use a fusion layer to merge the mid-level and global features. \(G\) can produce numerous images that are similar to the training data; therefore, \(D\) can learn better representations of remotely sensed images using the training data provided by \(G\). The classification results on two widely used remote sensing image databases show that the proposed method significantly improves the classification performance compared with other state-of-the-art methods.</description><identifier>EISSN: 2331-8422</identifier><identifier>DOI: 10.48550/arxiv.1612.08879</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Artificial neural networks ; Computer Science - Computer Vision and Pattern Recognition ; Detection ; Feature extraction ; Image classification ; Machine learning ; Remote sensing ; Representations ; Training</subject><ispartof>arXiv.org, 2017-11</ispartof><rights>2017. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,781,785,886,27930</link.rule.ids><backlink>$$Uhttps://doi.org/10.48550/arXiv.1612.08879$$DView paper in arXiv$$Hfree_for_read</backlink><backlink>$$Uhttps://doi.org/10.1109/LGRS.2017.2752750$$DView published paper (Access to full text may be restricted)$$Hfree_for_read</backlink></links><search><creatorcontrib>Lin, Daoyu</creatorcontrib><creatorcontrib>Fu, Kun</creatorcontrib><creatorcontrib>Wang, Yang</creatorcontrib><creatorcontrib>Xu, Guangluan</creatorcontrib><creatorcontrib>Sun, Xian</creatorcontrib><title>MARTA GANs: Unsupervised Representation Learning for Remote Sensing Image Classification</title><title>arXiv.org</title><description>With the development of deep learning, supervised learning has frequently been adopted to classify remotely sensed images using convolutional networks (CNNs). However, due to the limited amount of labeled data available, supervised learning is often difficult to carry out. Therefore, we proposed an unsupervised model called multiple-layer feature-matching generative adversarial networks (MARTA GANs) to learn a representation using only unlabeled data. MARTA GANs consists of both a generative model \(G\) and a discriminative model \(D\). We treat \(D\) as a feature extractor. To fit the complex properties of remote sensing data, we use a fusion layer to merge the mid-level and global features. \(G\) can produce numerous images that are similar to the training data; therefore, \(D\) can learn better representations of remotely sensed images using the training data provided by \(G\). The classification results on two widely used remote sensing image databases show that the proposed method significantly improves the classification performance compared with other state-of-the-art methods.</description><subject>Artificial neural networks</subject><subject>Computer Science - Computer Vision and Pattern Recognition</subject><subject>Detection</subject><subject>Feature extraction</subject><subject>Image classification</subject><subject>Machine learning</subject><subject>Remote sensing</subject><subject>Representations</subject><subject>Training</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GOX</sourceid><recordid>eNotj8tKAzEUhoMgWGofwJUB11MzyeTmbijaFqpCreBuSDMnJaXNjMlM0be3F1c__JfD-RC6y8m4UJyTRxN__GGci5yOiVJSX6EBZSzPVEHpDRqltCWEUCEp52yAvl7L5arE0_ItPeHPkPoW4sEnqPES2ggJQmc63wS8ABODDxvsmnjM9k0H-ANCOlnzvdkAnuxMSt55ex7comtndglG_zpEq5fn1WSWLd6n80m5yAynLHMqVxyMLBxw4YiVa-CFNXWxBlUXhliQSgEhghKha9A219Rqw-1a1tJpYEN0fzl7xq7a6Pcm_lYn_OqMf2w8XBptbL57SF21bfoYjj9VlEihhZCMsT-lKF4n</recordid><startdate>20171121</startdate><enddate>20171121</enddate><creator>Lin, Daoyu</creator><creator>Fu, Kun</creator><creator>Wang, Yang</creator><creator>Xu, Guangluan</creator><creator>Sun, Xian</creator><general>Cornell University Library, arXiv.org</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20171121</creationdate><title>MARTA GANs: Unsupervised Representation Learning for Remote Sensing Image Classification</title><author>Lin, Daoyu ; Fu, Kun ; Wang, Yang ; Xu, Guangluan ; Sun, Xian</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a523-f8185ea74fe56f0c7be54cad4be8d4a0ce788e0062069de9c192c9a5cb7d7f9e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Artificial neural networks</topic><topic>Computer Science - Computer Vision and Pattern Recognition</topic><topic>Detection</topic><topic>Feature extraction</topic><topic>Image classification</topic><topic>Machine learning</topic><topic>Remote sensing</topic><topic>Representations</topic><topic>Training</topic><toplevel>online_resources</toplevel><creatorcontrib>Lin, Daoyu</creatorcontrib><creatorcontrib>Fu, Kun</creatorcontrib><creatorcontrib>Wang, Yang</creatorcontrib><creatorcontrib>Xu, Guangluan</creatorcontrib><creatorcontrib>Sun, Xian</creatorcontrib><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>arXiv Computer Science</collection><collection>arXiv.org</collection><jtitle>arXiv.org</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lin, Daoyu</au><au>Fu, Kun</au><au>Wang, Yang</au><au>Xu, Guangluan</au><au>Sun, Xian</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>MARTA GANs: Unsupervised Representation Learning for Remote Sensing Image Classification</atitle><jtitle>arXiv.org</jtitle><date>2017-11-21</date><risdate>2017</risdate><eissn>2331-8422</eissn><abstract>With the development of deep learning, supervised learning has frequently been adopted to classify remotely sensed images using convolutional networks (CNNs). However, due to the limited amount of labeled data available, supervised learning is often difficult to carry out. Therefore, we proposed an unsupervised model called multiple-layer feature-matching generative adversarial networks (MARTA GANs) to learn a representation using only unlabeled data. MARTA GANs consists of both a generative model \(G\) and a discriminative model \(D\). We treat \(D\) as a feature extractor. To fit the complex properties of remote sensing data, we use a fusion layer to merge the mid-level and global features. \(G\) can produce numerous images that are similar to the training data; therefore, \(D\) can learn better representations of remotely sensed images using the training data provided by \(G\). The classification results on two widely used remote sensing image databases show that the proposed method significantly improves the classification performance compared with other state-of-the-art methods.</abstract><cop>Ithaca</cop><pub>Cornell University Library, arXiv.org</pub><doi>10.48550/arxiv.1612.08879</doi><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier EISSN: 2331-8422
ispartof arXiv.org, 2017-11
issn 2331-8422
language eng
recordid cdi_arxiv_primary_1612_08879
source Freely Accessible Journals; arXiv.org
subjects Artificial neural networks
Computer Science - Computer Vision and Pattern Recognition
Detection
Feature extraction
Image classification
Machine learning
Remote sensing
Representations
Training
title MARTA GANs: Unsupervised Representation Learning for Remote Sensing Image Classification
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-15T08%3A38%3A36IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_arxiv&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=MARTA%20GANs:%20Unsupervised%20Representation%20Learning%20for%20Remote%20Sensing%20Image%20Classification&rft.jtitle=arXiv.org&rft.au=Lin,%20Daoyu&rft.date=2017-11-21&rft.eissn=2331-8422&rft_id=info:doi/10.48550/arxiv.1612.08879&rft_dat=%3Cproquest_arxiv%3E2076966733%3C/proquest_arxiv%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2076966733&rft_id=info:pmid/&rfr_iscdi=true