DNBとBTデータを用いたANNによる漁火検出モデルについて
Saved in:
Published in | 写真測量とリモートセンシング Vol. 58; no. 1; pp. 4 - 13 |
---|---|
Main Authors | , , , , |
Format | Journal Article |
Language | Japanese |
Published |
一般社団法人 日本写真測量学会
2019
|
Online Access | Get full text |
ISSN | 0285-5844 1883-9061 |
DOI | 10.4287/jsprs.58.4 |
Cover
Author | 長谷川, 大輔 朴, 鍾杰 山口, 崇志 浅沼, 市男 マッキン, ケネス ジェームス |
---|---|
Author_xml | – sequence: 1 fullname: 長谷川, 大輔 organization: 東京情報大学 – sequence: 1 fullname: 朴, 鍾杰 organization: 東京情報大学 – sequence: 1 fullname: マッキン, ケネス ジェームス organization: 東京情報大学 – sequence: 1 fullname: 山口, 崇志 organization: 東京情報大学 – sequence: 1 fullname: 浅沼, 市男 organization: 東京情報大学 |
BookMark | eNo9kM1Kw0AUhQepYK3d-B6p85vMLFy09Z8SN3UdJpOJNtRakm5cTipUcCcuBUGKFPIGUvBhBtvXMI3i5lw45-Pey9kFtdHdSAOwj2CLYu4dJNk4zVqMt-gWqCPOiSOgi2qgDjFnDuOU7oBmlg1CiCH1MBekDi6O_I41i07fTmd2urT5l82f1y8Lax6seWv7vjWFzR9t_rRamrUpVvPX79mnnb5XfLFJzbyCP_bAdiyHmW7-zQa4Ojnud8-c3uXpebfdc5LyrHCQFETEOtZaY6lcgSIdK1l-W2roESZcBYnCEVPcCwliTEEcURdGnMYxxog0wOHv3iSbyGsdjNPBrUzvA5lOBmqog6qGgPEAbYT---pGpkEiyQ-VO3Bl |
ContentType | Journal Article |
Copyright | 2019 一般社団法人 日本写真測量学会 |
Copyright_xml | – notice: 2019 一般社団法人 日本写真測量学会 |
DOI | 10.4287/jsprs.58.4 |
DeliveryMethod | fulltext_linktorsrc |
Discipline | Visual Arts |
EISSN | 1883-9061 |
EndPage | 13 |
ExternalDocumentID | article_jsprs_58_1_58_4_article_char_ja |
GroupedDBID | ABJNI ACGFS ALMA_UNASSIGNED_HOLDINGS JSF KQ8 RJT ~02 |
ID | FETCH-LOGICAL-j2049-1a939fefeee2ac691defca883fcab73596c03c2d5c87b3155c02d460d84ff2213 |
ISSN | 0285-5844 |
IngestDate | Wed Sep 03 06:04:50 EDT 2025 |
IsDoiOpenAccess | true |
IsOpenAccess | true |
IsPeerReviewed | false |
IsScholarly | true |
Issue | 1 |
Language | Japanese |
LinkModel | OpenURL |
MergedId | FETCHMERGED-LOGICAL-j2049-1a939fefeee2ac691defca883fcab73596c03c2d5c87b3155c02d460d84ff2213 |
OpenAccessLink | https://www.jstage.jst.go.jp/article/jsprs/58/1/58_4/_article/-char/ja |
PageCount | 10 |
ParticipantIDs | jstage_primary_article_jsprs_58_1_58_4_article_char_ja |
PublicationCentury | 2000 |
PublicationDate | 2019 |
PublicationDateYYYYMMDD | 2019-01-01 |
PublicationDate_xml | – year: 2019 text: 2019 |
PublicationDecade | 2010 |
PublicationTitle | 写真測量とリモートセンシング |
PublicationTitleAlternate | 写真測量とリモートセンシング |
PublicationYear | 2019 |
Publisher | 一般社団法人 日本写真測量学会 |
Publisher_xml | – name: 一般社団法人 日本写真測量学会 |
References | 5) CEARAC, 2005. Integrated Report on Ocean Remote Sensing for the NOWPAP Region, 50pp. 9) Fu, G., Liu, C., Zhou, R., Sun, T., Zhang, Q., 2017. Classification for High Resolution Remote Sensing Imagery Using a Fully Convolutional Network. Remote Sens., 9(5), 498, 21pp, doi : 10.3390/rs9050498. 17) Straka, W.C., Seaman, C., Baugh, K., Cole, K., Stevens, E., Miller, S.D., 2015. Utilization of the Suomi National Polar-Orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band for Arctic Ship Tracking and Fisheries Management, Remote Sens., 7(1), pp. 971-989, doi : 10.3390/rs70100971. 6) Czyzowska-Wisniewski, E.H., van Leeuwen, W.J.D., Hirschboeck, K.K., Marsh, S.E., & Wisniewski, W.T., 2015. Fractional snow cover estimation in complex alpine-forested environments using an artificial neural network, Remote Sensing of Environment, 156, pp. 403-417, doi : 10.1016/j.rse.2014.09.0262015. 18) Yuan, H., Yang, G., Li, C., Wang, Y., Liu, J., Yu, H., Feng, H., Xu, B., Zhao, X., Yang, X., 2017. Retrieving Soybean Leaf Area Index from Unmanned Aerial Vehicle Hyperspectral Remote Sensing : Analysis of RF, ANN, and SVM Regression Models, Remote Sens., 9(4), 309, 14pp, doi : 10.3390/rs9040309. 7) Dang T.N., 2012. Fisheries Co-operation in the South China Sea and the (Ir) relevance of the Sovereignty Question, Asian Journal of International Law, 2(1), pp. 59-88. 14) Muallil, R.N., Mamauag, S.S., Cababaro, J.T., Arceo, H.O., and Alino, P.M., 2014. Catch trends in Philippine small-scale fisheries over the last five decades : The Fishers'perspective, Marine Policy, 47, pp. 110-117. 1) 浅沼市男,2013.Visible Infrared Imaging Radiometer Suits(VIIRS)による漁火観測,写真測量とリモートセンシング52(3),pp. 106-107 3) Albu, R-D., 2009. Human Face Recognition Using Convolutional Neural Networks, Journal of Electrical and Electronics Engineering 2(2), pp. 110-113. 11) LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. (1998). Gradient-Based Learning Applied to Document Recognition. Proceedings of the IEEE, 86(11), pp. 2278-2324. 4) Asanuma, I., T. Yamaguchi, J.G. Park, K. Mackin, J. Mittleman., 2017. Detection of Temporal Change of Fishery and Island Activities by DNB and SAR on the South China Sea, International Science Index International Journal of Computer, Electrical, Automation, Control and Information Engineering, 11(2), pp. 252-255. 10) Jarrett, K., Kavukcuoglu, K., Ranzato, M., LeCun, Y., 2009. What is the best multi-stage architecture for object recognition? In ICCV, pp. 2146-2153, doi : 10.1109/ICCV.2009.5459469. 15) Nesterov, Y., 1983. A method of solving a convex programming problem with convergence rate O(1/k2), Soviet Mathematics Doklady, 27(2), pp. 372-376. 16) Rosenberg, D., 1999. Environmental Pollution around the South China Sea : Developing a Regional Response to a regional problem Series : Resource Management in Asia-Pacific Working Paper, 20, doi : 10.1355/C521-1F. 8) Elvidge, C.D., Zhizhin, M., Baugh, K., Hsu, F.C., 2015. Automatic Boat Identification System for VIIRS Low Light Imaging Data. Remote Sens., 7(3), pp. 3020-3036, doi : 10.3390/rs70303020. 13) Meeus, J., 1998. Astronomical algorithms (2nd ed.), Willmann-Bell, Richmond, 477pp. 12) Liu, Y., Zhong, Y., Fei, F., Zhu, Q., Qin, Q., 2018. Scene Classification Based on a Deep Random-Scale Stretched Convolutional Neural Network. Remote Sens., 10(3), 444, 23pp, doi : 10.3390/rs10030444. 2) 山田陽巳,2015.東シナ海・黄海の漁業資源(総説),水産庁・水産総合研究センター,平成26年度国際漁業資源の現況,63,pp. 1-6 |
References_xml | – reference: 7) Dang T.N., 2012. Fisheries Co-operation in the South China Sea and the (Ir) relevance of the Sovereignty Question, Asian Journal of International Law, 2(1), pp. 59-88. – reference: 4) Asanuma, I., T. Yamaguchi, J.G. Park, K. Mackin, J. Mittleman., 2017. Detection of Temporal Change of Fishery and Island Activities by DNB and SAR on the South China Sea, International Science Index International Journal of Computer, Electrical, Automation, Control and Information Engineering, 11(2), pp. 252-255. – reference: 6) Czyzowska-Wisniewski, E.H., van Leeuwen, W.J.D., Hirschboeck, K.K., Marsh, S.E., & Wisniewski, W.T., 2015. Fractional snow cover estimation in complex alpine-forested environments using an artificial neural network, Remote Sensing of Environment, 156, pp. 403-417, doi : 10.1016/j.rse.2014.09.0262015. – reference: 17) Straka, W.C., Seaman, C., Baugh, K., Cole, K., Stevens, E., Miller, S.D., 2015. Utilization of the Suomi National Polar-Orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band for Arctic Ship Tracking and Fisheries Management, Remote Sens., 7(1), pp. 971-989, doi : 10.3390/rs70100971. – reference: 10) Jarrett, K., Kavukcuoglu, K., Ranzato, M., LeCun, Y., 2009. What is the best multi-stage architecture for object recognition? In ICCV, pp. 2146-2153, doi : 10.1109/ICCV.2009.5459469. – reference: 13) Meeus, J., 1998. Astronomical algorithms (2nd ed.), Willmann-Bell, Richmond, 477pp. – reference: 18) Yuan, H., Yang, G., Li, C., Wang, Y., Liu, J., Yu, H., Feng, H., Xu, B., Zhao, X., Yang, X., 2017. Retrieving Soybean Leaf Area Index from Unmanned Aerial Vehicle Hyperspectral Remote Sensing : Analysis of RF, ANN, and SVM Regression Models, Remote Sens., 9(4), 309, 14pp, doi : 10.3390/rs9040309. – reference: 9) Fu, G., Liu, C., Zhou, R., Sun, T., Zhang, Q., 2017. Classification for High Resolution Remote Sensing Imagery Using a Fully Convolutional Network. Remote Sens., 9(5), 498, 21pp, doi : 10.3390/rs9050498. – reference: 16) Rosenberg, D., 1999. Environmental Pollution around the South China Sea : Developing a Regional Response to a regional problem Series : Resource Management in Asia-Pacific Working Paper, 20, doi : 10.1355/C521-1F. – reference: 1) 浅沼市男,2013.Visible Infrared Imaging Radiometer Suits(VIIRS)による漁火観測,写真測量とリモートセンシング52(3),pp. 106-107. – reference: 14) Muallil, R.N., Mamauag, S.S., Cababaro, J.T., Arceo, H.O., and Alino, P.M., 2014. Catch trends in Philippine small-scale fisheries over the last five decades : The Fishers'perspective, Marine Policy, 47, pp. 110-117. – reference: 3) Albu, R-D., 2009. Human Face Recognition Using Convolutional Neural Networks, Journal of Electrical and Electronics Engineering 2(2), pp. 110-113. – reference: 12) Liu, Y., Zhong, Y., Fei, F., Zhu, Q., Qin, Q., 2018. Scene Classification Based on a Deep Random-Scale Stretched Convolutional Neural Network. Remote Sens., 10(3), 444, 23pp, doi : 10.3390/rs10030444. – reference: 5) CEARAC, 2005. Integrated Report on Ocean Remote Sensing for the NOWPAP Region, 50pp. – reference: 15) Nesterov, Y., 1983. A method of solving a convex programming problem with convergence rate O(1/k2), Soviet Mathematics Doklady, 27(2), pp. 372-376. – reference: 2) 山田陽巳,2015.東シナ海・黄海の漁業資源(総説),水産庁・水産総合研究センター,平成26年度国際漁業資源の現況,63,pp. 1-6. – reference: 8) Elvidge, C.D., Zhizhin, M., Baugh, K., Hsu, F.C., 2015. Automatic Boat Identification System for VIIRS Low Light Imaging Data. Remote Sens., 7(3), pp. 3020-3036, doi : 10.3390/rs70303020. – reference: 11) LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. (1998). Gradient-Based Learning Applied to Document Recognition. Proceedings of the IEEE, 86(11), pp. 2278-2324. |
SSID | ssib020472893 ssib036263174 ssib004305393 ssj0000400259 ssib031741132 |
Score | 2.171052 |
SourceID | jstage |
SourceType | Publisher |
StartPage | 4 |
Title | DNBとBTデータを用いたANNによる漁火検出モデルについて |
URI | https://www.jstage.jst.go.jp/article/jsprs/58/1/58_4/_article/-char/ja |
Volume | 58 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
ispartofPNX | 写真測量とリモートセンシング, 2019, Vol.58(1), pp.4-13 |
journalDatabaseRights | – providerCode: PRVAFT databaseName: Open Access Digital Library customDbUrl: eissn: 1883-9061 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0000400259 issn: 0285-5844 databaseCode: KQ8 dateStart: 19750101 isFulltext: true titleUrlDefault: http://grweb.coalliance.org/oadl/oadl.html providerName: Colorado Alliance of Research Libraries |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnR3Latww0IT00h5Kn_RNDtWpOLUtyZaO8j4IgS4UNiU3s_ba0D2kIZtceineFFLorfRYKJRQAvmDEujHmCa_0RnJWjubHtKAMfJoNE_bM-OVtI7zfDSKIHLI1I28FAoUP4rcFBfneCktJK6czLSnXw3CtQ22vsk3l5Y_tGYt7e2mq9n7f64ruYpXAQZ-xVWy_-HZOVEAQBv8C2fwMJwv5ePuICY9CskgUSIe6iYcUd2IO7oRkLhfN2RAehGRDNDrcYLVDdlXg4ElFtf4QtgGQEIkCL1AocYJiWJEAheOTGNV81XBoiQqPk_Zx4ELAqiwnSdrmiGRUgvcAfG0AIIoYCeRsujPVbdcLghQW4A2isSx7aIWEl2AzH_BQVaSaxQBYN3Ql7KLtyZegCLKdIOQrBkYotQxM2ggcJfEPQ3tnqOvOcueFdOKoLqNUJqCEcy3eF0LkfM-q6GKFpVX3hy7YcyRGhLkaElFrT4gsjBa9omZ0my_CbViDukxZCg8VFwYp0RoCtSRExkjQfQXRfMhsoLjhdYf8HhtHtW5nJs52kOFmhDgqFbwCgR3Ibk1hs9NcBWCutIzm-_b6MvFhbeMCaWslZOZ5cqL0R6rfYz20-2d6SoXq6zJaeYzTetnM9FICReJjyeWWDiubUwmUGBdCyJIVXFWx-v2_ncQo5oAFuDWqq3NmiD3Zr7fLMzWWzv5dT2lU0GGNYUuwa09zBbJKPrLRnBIfydQDNqJpDq3Hd5ybtZF6Yoy0t52liajO86NN2-newY6veusw7umKo_iYbV_UO2fVLPf1ezL2dejqvxYld_h3VGVx9XsUzX7fHpSnpXHp4ff_hz8qvZ_aPxj7C0PNfLPe85GvzfsrLn1_7C4E9BYuv5IUlnkRZ7nwSgLpT_Oi2wEzoRzGlEuw8yjWTDmmYhSCgVK5gVjFnpjwYoiCHx631neereVP3BWGGQUGRVgyjxnPB9LmkLKkQsvKwoYRh86oTFEsm0220ku6cBHVx342LmOT5D5qvrEWd7d2cufQp2xmz7T98JfEhvg4A |
linkProvider | Colorado Alliance of Research Libraries |
openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=DNB%E3%81%A8BT%E3%83%87%E3%83%BC%E3%82%BF%E3%82%92%E7%94%A8%E3%81%84%E3%81%9FANN%E3%81%AB%E3%82%88%E3%82%8B%E6%BC%81%E7%81%AB%E6%A4%9C%E5%87%BA%E3%83%A2%E3%83%87%E3%83%AB%E3%81%AB%E3%81%A4%E3%81%84%E3%81%A6&rft.jtitle=%E5%86%99%E7%9C%9F%E6%B8%AC%E9%87%8F%E3%81%A8%E3%83%AA%E3%83%A2%E3%83%BC%E3%83%88%E3%82%BB%E3%83%B3%E3%82%B7%E3%83%B3%E3%82%B0&rft.au=%E9%95%B7%E8%B0%B7%E5%B7%9D%2C+%E5%A4%A7%E8%BC%94&rft.au=%E6%9C%B4%2C+%E9%8D%BE%E6%9D%B0&rft.au=%E3%83%9E%E3%83%83%E3%82%AD%E3%83%B3%2C+%E3%82%B1%E3%83%8D%E3%82%B9+%E3%82%B8%E3%82%A7%E3%83%BC%E3%83%A0%E3%82%B9&rft.au=%E5%B1%B1%E5%8F%A3%2C+%E5%B4%87%E5%BF%97&rft.date=2019&rft.pub=%E4%B8%80%E8%88%AC%E7%A4%BE%E5%9B%A3%E6%B3%95%E4%BA%BA+%E6%97%A5%E6%9C%AC%E5%86%99%E7%9C%9F%E6%B8%AC%E9%87%8F%E5%AD%A6%E4%BC%9A&rft.issn=0285-5844&rft.eissn=1883-9061&rft.volume=58&rft.issue=1&rft.spage=4&rft.epage=13&rft_id=info:doi/10.4287%2Fjsprs.58.4&rft.externalDocID=article_jsprs_58_1_58_4_article_char_ja |
thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0285-5844&client=summon |
thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0285-5844&client=summon |
thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0285-5844&client=summon |