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A Novel General Semisupervised Deep Learning Framework for Classification and Regression with Remote Sensing Images
Remote sensing image analysis often involves image-level classification and/ or regression. One major problem with remote sensing data is that it is difficult to obtain abundant precise manual annotations to train fully supervised deep networks that have achieved great success in computer vision. Th...
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| creator | Chen, Zhao Chen, Guangchen Zhou, Feng Yang, Bin Wang, Lili Liu, Qiong Chen, Yonghang |
| description | Remote sensing image analysis often involves image-level classification and/ or regression. One major problem with remote sensing data is that it is difficult to obtain abundant precise manual annotations to train fully supervised deep networks that have achieved great success in computer vision. Therefore, this paper proposes a novel general semisupervised framework (GSF) which only requires a small amount of annotated samples for training. It employs a new hybrid (dis) similarity to characterize different aspects of the images and realizes label propagation while fine- tuning a deep neural network (NN). As shown by the experimental results, GSF outperforms several supervised baselines and state-of-the-art semisupervised models in classification and regression. |
| doi_str_mv | 10.1109/IGARSS39084.2020.9323932 |
| format | conference_proceeding |
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As shown by the experimental results, GSF outperforms several supervised baselines and state-of-the-art semisupervised models in classification and regression.</description><subject>Artificial neural networks</subject><subject>classification</subject><subject>Cyclones</subject><subject>Estimation</subject><subject>infrared images</subject><subject>multispectral images</subject><subject>regression</subject><subject>Remote sensing</subject><subject>Semisupervised</subject><subject>Task analysis</subject><subject>Training</subject><subject>Wind speed</subject><issn>2153-7003</issn><isbn>1728163749</isbn><isbn>9781728163741</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2020</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotkM1OwzAQhA0SEm3hCbj4BRL8m9jHqtBQqQKphXNl4nUw5E92aMXbY0QPo51vtTuHQQhTklNK9P2mWu72e66JEjkjjOSaM550gea0ZIoWvBT6Es0YlTwrCeHXaB7jZzKKETJDcYmfhyO0uIIegmnxHjofv0cIRx_B4geAEW_BhN73DV4H08FpCF_YDQGvWhOjd742kx96bHqLd9AESMuEJz99JO6GCVJoH__-N51pIN6gK2faCLfnuUBv68fX1VO2fak2q-U284zwKbNSFFxQLYWkypZCCGNdraTlttSsBMXqQhAqJThjhauldYWu35UTWqZzzRfo7j_XA8BhDL4z4edwLoj_Avn5XPM</recordid><startdate>20200926</startdate><enddate>20200926</enddate><creator>Chen, Zhao</creator><creator>Chen, Guangchen</creator><creator>Zhou, Feng</creator><creator>Yang, Bin</creator><creator>Wang, Lili</creator><creator>Liu, Qiong</creator><creator>Chen, Yonghang</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>20200926</creationdate><title>A Novel General Semisupervised Deep Learning Framework for Classification and Regression with Remote Sensing Images</title><author>Chen, Zhao ; Chen, Guangchen ; Zhou, Feng ; Yang, Bin ; Wang, Lili ; Liu, Qiong ; Chen, Yonghang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i203t-d546341954518d7444adfc85d3d7927e82c640155efad4fc5df69cb8f495d7493</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Artificial neural networks</topic><topic>classification</topic><topic>Cyclones</topic><topic>Estimation</topic><topic>infrared images</topic><topic>multispectral images</topic><topic>regression</topic><topic>Remote sensing</topic><topic>Semisupervised</topic><topic>Task analysis</topic><topic>Training</topic><topic>Wind speed</topic><toplevel>online_resources</toplevel><creatorcontrib>Chen, Zhao</creatorcontrib><creatorcontrib>Chen, Guangchen</creatorcontrib><creatorcontrib>Zhou, Feng</creatorcontrib><creatorcontrib>Yang, Bin</creatorcontrib><creatorcontrib>Wang, Lili</creatorcontrib><creatorcontrib>Liu, Qiong</creatorcontrib><creatorcontrib>Chen, Yonghang</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE/IET Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Chen, Zhao</au><au>Chen, Guangchen</au><au>Zhou, Feng</au><au>Yang, Bin</au><au>Wang, Lili</au><au>Liu, Qiong</au><au>Chen, Yonghang</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>A Novel General Semisupervised Deep Learning Framework for Classification and Regression with Remote Sensing Images</atitle><btitle>IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium</btitle><stitle>IGARSS</stitle><date>2020-09-26</date><risdate>2020</risdate><spage>1323</spage><epage>1326</epage><pages>1323-1326</pages><eissn>2153-7003</eissn><eisbn>1728163749</eisbn><eisbn>9781728163741</eisbn><abstract>Remote sensing image analysis often involves image-level classification and/ or regression. One major problem with remote sensing data is that it is difficult to obtain abundant precise manual annotations to train fully supervised deep networks that have achieved great success in computer vision. Therefore, this paper proposes a novel general semisupervised framework (GSF) which only requires a small amount of annotated samples for training. It employs a new hybrid (dis) similarity to characterize different aspects of the images and realizes label propagation while fine- tuning a deep neural network (NN). As shown by the experimental results, GSF outperforms several supervised baselines and state-of-the-art semisupervised models in classification and regression.</abstract><pub>IEEE</pub><doi>10.1109/IGARSS39084.2020.9323932</doi><tpages>4</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | EISSN: 2153-7003 |
| ispartof | IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, 2020, p.1323-1326 |
| issn | 2153-7003 |
| language | eng |
| recordid | cdi_ieee_primary_9323932 |
| source | IEEE Xplore All Conference Series |
| subjects | Artificial neural networks classification Cyclones Estimation infrared images multispectral images regression Remote sensing Semisupervised Task analysis Training Wind speed |
| title | A Novel General Semisupervised Deep Learning Framework for Classification and Regression with Remote Sensing Images |
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