Adaptive Dual-path Collaborative Learning for PAN and MS Classification

Due to the limitation of sensor technology, researchers tend to obtain high-quality image information from panchromatic (PAN) images and multispectral (MS) images with different resolutions. Therefore, the classification of remote sensing images of PAN and MS have become a research hotspot. In this...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on geoscience and remote sensing 2022, Vol.60, p.1-1
Hauptverfasser: Zhu, Hao, Sun, Kenan, Jiao, Licheng, Li, Xiaotong, Liu, Fang, Hou, Biao, Wang, Shuang
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 1
container_issue
container_start_page 1
container_title IEEE transactions on geoscience and remote sensing
container_volume 60
creator Zhu, Hao
Sun, Kenan
Jiao, Licheng
Li, Xiaotong
Liu, Fang
Hou, Biao
Wang, Shuang
description Due to the limitation of sensor technology, researchers tend to obtain high-quality image information from panchromatic (PAN) images and multispectral (MS) images with different resolutions. Therefore, the classification of remote sensing images of PAN and MS have become a research hotspot. In this paper, we propose an adaptive dual-path collaborative learning method for PAN and MS classification. In the stage of sample generation and training, we propose an adaptive neighborhood sample grading (ANSG) strategy in the establishing sample stage so that each pixel to be classified can obtain neighborhood information suitable for itself. Further, to simulate biological cognitive mechanisms, we divide the samples into different levels, and design the self-paced progressive loss (SPL), thus allowing the network to do preference training in different stages. The network's training can quickly reach the optimal of the current stage and the overall convergence is more thorough. In the network structure, we propose a dual-path module (DPM) to effectively alleviate the gradient degradation in the residual path , while ensuring maximum gradient loss information flow between every two layers in the densely connected path . This module can extract more robust features to cope with the complex characteristics of remote-sensing images. Moreover, using the characteristics of the dual path to better fuse the features by the gradual collaborative fusion (GCF) way. The experimental results and theoretical analysis have demonstrated the proposed approach's effectiveness, feasibility, and robustness. Our model are available at https://github.com/AIpy-nan/DBFI-Net.
doi_str_mv 10.1109/TGRS.2022.3223921
format Article
fullrecord <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_ieee_primary_9957095</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>9957095</ieee_id><sourcerecordid>2745128110</sourcerecordid><originalsourceid>FETCH-LOGICAL-c293t-e48d13ba540bd28d989cb91834ca4f292fbfb34816e4feb2d51c280ad09a3dfa3</originalsourceid><addsrcrecordid>eNo9kE1LAzEQhoMoWKs_QLwseN6aTJI2OZZVq1A_sPUcJptEV9bdNdkK_nu3tngaGJ73HeYh5JzRCWNUX60XL6sJUIAJB-Aa2AEZMSlVTqdCHJIRZXqag9JwTE5S-qCUCclmI7KYO-z66ttn1xus8w7796xo6xptG_Fvv_QYm6p5y0Ibs-f5Y4aNyx5WWVFjSlWoygFrm1NyFLBO_mw_x-T19mZd3OXLp8V9MV_mJWje514ox7hFKah1oJxWurSaKS5KFAE0BBssF4pNvQjegpOsBEXRUY3cBeRjcrnr7WL7tfGpNx_tJjbDSQOz4SVQg46BYjuqjG1K0QfTxeoT449h1Gx9ma0vs_Vl9r6GzMUuU3nv_3mt5YxqyX8BVThl-w</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2745128110</pqid></control><display><type>article</type><title>Adaptive Dual-path Collaborative Learning for PAN and MS Classification</title><source>IEEE Electronic Library Online</source><creator>Zhu, Hao ; Sun, Kenan ; Jiao, Licheng ; Li, Xiaotong ; Liu, Fang ; Hou, Biao ; Wang, Shuang</creator><creatorcontrib>Zhu, Hao ; Sun, Kenan ; Jiao, Licheng ; Li, Xiaotong ; Liu, Fang ; Hou, Biao ; Wang, Shuang</creatorcontrib><description>Due to the limitation of sensor technology, researchers tend to obtain high-quality image information from panchromatic (PAN) images and multispectral (MS) images with different resolutions. Therefore, the classification of remote sensing images of PAN and MS have become a research hotspot. In this paper, we propose an adaptive dual-path collaborative learning method for PAN and MS classification. In the stage of sample generation and training, we propose an adaptive neighborhood sample grading (ANSG) strategy in the establishing sample stage so that each pixel to be classified can obtain neighborhood information suitable for itself. Further, to simulate biological cognitive mechanisms, we divide the samples into different levels, and design the self-paced progressive loss (SPL), thus allowing the network to do preference training in different stages. The network's training can quickly reach the optimal of the current stage and the overall convergence is more thorough. In the network structure, we propose a dual-path module (DPM) to effectively alleviate the gradient degradation in the residual path , while ensuring maximum gradient loss information flow between every two layers in the densely connected path . This module can extract more robust features to cope with the complex characteristics of remote-sensing images. Moreover, using the characteristics of the dual path to better fuse the features by the gradual collaborative fusion (GCF) way. The experimental results and theoretical analysis have demonstrated the proposed approach's effectiveness, feasibility, and robustness. Our model are available at https://github.com/AIpy-nan/DBFI-Net.</description><identifier>ISSN: 0196-2892</identifier><identifier>EISSN: 1558-0644</identifier><identifier>DOI: 10.1109/TGRS.2022.3223921</identifier><identifier>CODEN: IGRSD2</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Adaptive sampling ; Classification ; Classification algorithms ; Cognitive ability ; Collaborative learning ; Deep Learning ; Distortion ; Dogs ; Feature extraction ; Feature Fusion ; Image classification ; Image quality ; Information flow ; Learning ; Mathematical models ; Modules ; PAN and MS Classification ; Remote Sensing ; Sample Strategy ; Sensors ; Theoretical analysis ; Training</subject><ispartof>IEEE transactions on geoscience and remote sensing, 2022, Vol.60, p.1-1</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c293t-e48d13ba540bd28d989cb91834ca4f292fbfb34816e4feb2d51c280ad09a3dfa3</citedby><cites>FETCH-LOGICAL-c293t-e48d13ba540bd28d989cb91834ca4f292fbfb34816e4feb2d51c280ad09a3dfa3</cites><orcidid>0000-0003-4411-0933 ; 0000-0001-9960-5373 ; 0000-0002-1996-186X ; 0000-0003-4940-1211 ; 0000-0002-5669-9354 ; 0000-0003-3354-9617</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9957095$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,4024,27923,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9957095$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Zhu, Hao</creatorcontrib><creatorcontrib>Sun, Kenan</creatorcontrib><creatorcontrib>Jiao, Licheng</creatorcontrib><creatorcontrib>Li, Xiaotong</creatorcontrib><creatorcontrib>Liu, Fang</creatorcontrib><creatorcontrib>Hou, Biao</creatorcontrib><creatorcontrib>Wang, Shuang</creatorcontrib><title>Adaptive Dual-path Collaborative Learning for PAN and MS Classification</title><title>IEEE transactions on geoscience and remote sensing</title><addtitle>TGRS</addtitle><description>Due to the limitation of sensor technology, researchers tend to obtain high-quality image information from panchromatic (PAN) images and multispectral (MS) images with different resolutions. Therefore, the classification of remote sensing images of PAN and MS have become a research hotspot. In this paper, we propose an adaptive dual-path collaborative learning method for PAN and MS classification. In the stage of sample generation and training, we propose an adaptive neighborhood sample grading (ANSG) strategy in the establishing sample stage so that each pixel to be classified can obtain neighborhood information suitable for itself. Further, to simulate biological cognitive mechanisms, we divide the samples into different levels, and design the self-paced progressive loss (SPL), thus allowing the network to do preference training in different stages. The network's training can quickly reach the optimal of the current stage and the overall convergence is more thorough. In the network structure, we propose a dual-path module (DPM) to effectively alleviate the gradient degradation in the residual path , while ensuring maximum gradient loss information flow between every two layers in the densely connected path . This module can extract more robust features to cope with the complex characteristics of remote-sensing images. Moreover, using the characteristics of the dual path to better fuse the features by the gradual collaborative fusion (GCF) way. The experimental results and theoretical analysis have demonstrated the proposed approach's effectiveness, feasibility, and robustness. Our model are available at https://github.com/AIpy-nan/DBFI-Net.</description><subject>Adaptive sampling</subject><subject>Classification</subject><subject>Classification algorithms</subject><subject>Cognitive ability</subject><subject>Collaborative learning</subject><subject>Deep Learning</subject><subject>Distortion</subject><subject>Dogs</subject><subject>Feature extraction</subject><subject>Feature Fusion</subject><subject>Image classification</subject><subject>Image quality</subject><subject>Information flow</subject><subject>Learning</subject><subject>Mathematical models</subject><subject>Modules</subject><subject>PAN and MS Classification</subject><subject>Remote Sensing</subject><subject>Sample Strategy</subject><subject>Sensors</subject><subject>Theoretical analysis</subject><subject>Training</subject><issn>0196-2892</issn><issn>1558-0644</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kE1LAzEQhoMoWKs_QLwseN6aTJI2OZZVq1A_sPUcJptEV9bdNdkK_nu3tngaGJ73HeYh5JzRCWNUX60XL6sJUIAJB-Aa2AEZMSlVTqdCHJIRZXqag9JwTE5S-qCUCclmI7KYO-z66ttn1xus8w7796xo6xptG_Fvv_QYm6p5y0Ibs-f5Y4aNyx5WWVFjSlWoygFrm1NyFLBO_mw_x-T19mZd3OXLp8V9MV_mJWje514ox7hFKah1oJxWurSaKS5KFAE0BBssF4pNvQjegpOsBEXRUY3cBeRjcrnr7WL7tfGpNx_tJjbDSQOz4SVQg46BYjuqjG1K0QfTxeoT449h1Gx9ma0vs_Vl9r6GzMUuU3nv_3mt5YxqyX8BVThl-w</recordid><startdate>2022</startdate><enddate>2022</enddate><creator>Zhu, Hao</creator><creator>Sun, Kenan</creator><creator>Jiao, Licheng</creator><creator>Li, Xiaotong</creator><creator>Liu, Fang</creator><creator>Hou, Biao</creator><creator>Wang, Shuang</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7UA</scope><scope>8FD</scope><scope>C1K</scope><scope>F1W</scope><scope>FR3</scope><scope>H8D</scope><scope>H96</scope><scope>KR7</scope><scope>L.G</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0003-4411-0933</orcidid><orcidid>https://orcid.org/0000-0001-9960-5373</orcidid><orcidid>https://orcid.org/0000-0002-1996-186X</orcidid><orcidid>https://orcid.org/0000-0003-4940-1211</orcidid><orcidid>https://orcid.org/0000-0002-5669-9354</orcidid><orcidid>https://orcid.org/0000-0003-3354-9617</orcidid></search><sort><creationdate>2022</creationdate><title>Adaptive Dual-path Collaborative Learning for PAN and MS Classification</title><author>Zhu, Hao ; Sun, Kenan ; Jiao, Licheng ; Li, Xiaotong ; Liu, Fang ; Hou, Biao ; Wang, Shuang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c293t-e48d13ba540bd28d989cb91834ca4f292fbfb34816e4feb2d51c280ad09a3dfa3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Adaptive sampling</topic><topic>Classification</topic><topic>Classification algorithms</topic><topic>Cognitive ability</topic><topic>Collaborative learning</topic><topic>Deep Learning</topic><topic>Distortion</topic><topic>Dogs</topic><topic>Feature extraction</topic><topic>Feature Fusion</topic><topic>Image classification</topic><topic>Image quality</topic><topic>Information flow</topic><topic>Learning</topic><topic>Mathematical models</topic><topic>Modules</topic><topic>PAN and MS Classification</topic><topic>Remote Sensing</topic><topic>Sample Strategy</topic><topic>Sensors</topic><topic>Theoretical analysis</topic><topic>Training</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhu, Hao</creatorcontrib><creatorcontrib>Sun, Kenan</creatorcontrib><creatorcontrib>Jiao, Licheng</creatorcontrib><creatorcontrib>Li, Xiaotong</creatorcontrib><creatorcontrib>Liu, Fang</creatorcontrib><creatorcontrib>Hou, Biao</creatorcontrib><creatorcontrib>Wang, Shuang</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998–Present</collection><collection>IEEE Electronic Library Online</collection><collection>CrossRef</collection><collection>Water Resources Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Aquatic Science &amp; Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy &amp; Non-Living Resources</collection><collection>Civil Engineering Abstracts</collection><collection>Aquatic Science &amp; Fisheries Abstracts (ASFA) Professional</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>IEEE transactions on geoscience and remote sensing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Zhu, Hao</au><au>Sun, Kenan</au><au>Jiao, Licheng</au><au>Li, Xiaotong</au><au>Liu, Fang</au><au>Hou, Biao</au><au>Wang, Shuang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Adaptive Dual-path Collaborative Learning for PAN and MS Classification</atitle><jtitle>IEEE transactions on geoscience and remote sensing</jtitle><stitle>TGRS</stitle><date>2022</date><risdate>2022</risdate><volume>60</volume><spage>1</spage><epage>1</epage><pages>1-1</pages><issn>0196-2892</issn><eissn>1558-0644</eissn><coden>IGRSD2</coden><abstract>Due to the limitation of sensor technology, researchers tend to obtain high-quality image information from panchromatic (PAN) images and multispectral (MS) images with different resolutions. Therefore, the classification of remote sensing images of PAN and MS have become a research hotspot. In this paper, we propose an adaptive dual-path collaborative learning method for PAN and MS classification. In the stage of sample generation and training, we propose an adaptive neighborhood sample grading (ANSG) strategy in the establishing sample stage so that each pixel to be classified can obtain neighborhood information suitable for itself. Further, to simulate biological cognitive mechanisms, we divide the samples into different levels, and design the self-paced progressive loss (SPL), thus allowing the network to do preference training in different stages. The network's training can quickly reach the optimal of the current stage and the overall convergence is more thorough. In the network structure, we propose a dual-path module (DPM) to effectively alleviate the gradient degradation in the residual path , while ensuring maximum gradient loss information flow between every two layers in the densely connected path . This module can extract more robust features to cope with the complex characteristics of remote-sensing images. Moreover, using the characteristics of the dual path to better fuse the features by the gradual collaborative fusion (GCF) way. The experimental results and theoretical analysis have demonstrated the proposed approach's effectiveness, feasibility, and robustness. Our model are available at https://github.com/AIpy-nan/DBFI-Net.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TGRS.2022.3223921</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0003-4411-0933</orcidid><orcidid>https://orcid.org/0000-0001-9960-5373</orcidid><orcidid>https://orcid.org/0000-0002-1996-186X</orcidid><orcidid>https://orcid.org/0000-0003-4940-1211</orcidid><orcidid>https://orcid.org/0000-0002-5669-9354</orcidid><orcidid>https://orcid.org/0000-0003-3354-9617</orcidid></addata></record>
fulltext fulltext_linktorsrc
identifier ISSN: 0196-2892
ispartof IEEE transactions on geoscience and remote sensing, 2022, Vol.60, p.1-1
issn 0196-2892
1558-0644
language eng
recordid cdi_ieee_primary_9957095
source IEEE Electronic Library Online
subjects Adaptive sampling
Classification
Classification algorithms
Cognitive ability
Collaborative learning
Deep Learning
Distortion
Dogs
Feature extraction
Feature Fusion
Image classification
Image quality
Information flow
Learning
Mathematical models
Modules
PAN and MS Classification
Remote Sensing
Sample Strategy
Sensors
Theoretical analysis
Training
title Adaptive Dual-path Collaborative Learning for PAN and MS Classification
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-25T09%3A13%3A38IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Adaptive%20Dual-path%20Collaborative%20Learning%20for%20PAN%20and%20MS%20Classification&rft.jtitle=IEEE%20transactions%20on%20geoscience%20and%20remote%20sensing&rft.au=Zhu,%20Hao&rft.date=2022&rft.volume=60&rft.spage=1&rft.epage=1&rft.pages=1-1&rft.issn=0196-2892&rft.eissn=1558-0644&rft.coden=IGRSD2&rft_id=info:doi/10.1109/TGRS.2022.3223921&rft_dat=%3Cproquest_RIE%3E2745128110%3C/proquest_RIE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2745128110&rft_id=info:pmid/&rft_ieee_id=9957095&rfr_iscdi=true