A Ship Detector Design Based on Deep Convolutional Neural Networks for Satellite Images
Ship targetclassification from satellite images is a challenging task with itsrequirements of feature extracting, advanced pre-processing, a variety ofparameters obtained from satellites and other type of images, and analyzing ofimages. The dissimilarity of results, enhanced dataset requirement, int...
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Sakarya University
2020-02-01
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| Series: | Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi |
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| Online Access: | https://dergipark.org.tr/tr/download/article-file/967515 |
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| _version_ | 1846111485630087168 |
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| author | Ferhat Ucar Deniz Korkmaz |
| author_facet | Ferhat Ucar Deniz Korkmaz |
| author_sort | Ferhat Ucar |
| collection | DOAJ |
| description | Ship targetclassification from satellite images is a challenging task with itsrequirements of feature extracting, advanced pre-processing, a variety ofparameters obtained from satellites and other type of images, and analyzing ofimages. The dissimilarity of results, enhanced dataset requirement, intricacyof the problem domain, general use of Synthetic Aperture Radar (SAR) images andproblems on generalizability are some topics of the issues related to shiptarget detection. In this study, we propose a deep convolutional neural networkmodel for detecting the ships using the satellite images as inputs. Our model has acquired an adequate accuracyvalue by just using a pre-processed satellite image input. Visual and graphicalresults of features at various layers and deconvolutions are also demonstratedfor a better understanding of the basic process. |
| format | Article |
| id | doaj-art-88e6aef8b32e47a79395e8e709e2dbb7 |
| institution | Kabale University |
| issn | 2147-835X |
| language | English |
| publishDate | 2020-02-01 |
| publisher | Sakarya University |
| record_format | Article |
| series | Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi |
| spelling | doaj-art-88e6aef8b32e47a79395e8e709e2dbb72024-12-23T08:05:05ZengSakarya UniversitySakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi2147-835X2020-02-0124119720410.16984/saufenbilder.58773128A Ship Detector Design Based on Deep Convolutional Neural Networks for Satellite ImagesFerhat Ucar0https://orcid.org/0000-0001-9366-6124Deniz Korkmaz1https://orcid.org/0000-0002-5159-0659FIRAT UNIVERSITYMalatya Turgut Ozal University, Department of Electrical Engineering, Malatya, TurkeyShip targetclassification from satellite images is a challenging task with itsrequirements of feature extracting, advanced pre-processing, a variety ofparameters obtained from satellites and other type of images, and analyzing ofimages. The dissimilarity of results, enhanced dataset requirement, intricacyof the problem domain, general use of Synthetic Aperture Radar (SAR) images andproblems on generalizability are some topics of the issues related to shiptarget detection. In this study, we propose a deep convolutional neural networkmodel for detecting the ships using the satellite images as inputs. Our model has acquired an adequate accuracyvalue by just using a pre-processed satellite image input. Visual and graphicalresults of features at various layers and deconvolutions are also demonstratedfor a better understanding of the basic process.https://dergipark.org.tr/tr/download/article-file/967515deep convolutional neural networks (cnns)ship target classificationremote sensingsatellite imagery |
| spellingShingle | Ferhat Ucar Deniz Korkmaz A Ship Detector Design Based on Deep Convolutional Neural Networks for Satellite Images Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi deep convolutional neural networks (cnns) ship target classification remote sensing satellite imagery |
| title | A Ship Detector Design Based on Deep Convolutional Neural Networks for Satellite Images |
| title_full | A Ship Detector Design Based on Deep Convolutional Neural Networks for Satellite Images |
| title_fullStr | A Ship Detector Design Based on Deep Convolutional Neural Networks for Satellite Images |
| title_full_unstemmed | A Ship Detector Design Based on Deep Convolutional Neural Networks for Satellite Images |
| title_short | A Ship Detector Design Based on Deep Convolutional Neural Networks for Satellite Images |
| title_sort | ship detector design based on deep convolutional neural networks for satellite images |
| topic | deep convolutional neural networks (cnns) ship target classification remote sensing satellite imagery |
| url | https://dergipark.org.tr/tr/download/article-file/967515 |
| work_keys_str_mv | AT ferhatucar ashipdetectordesignbasedondeepconvolutionalneuralnetworksforsatelliteimages AT denizkorkmaz ashipdetectordesignbasedondeepconvolutionalneuralnetworksforsatelliteimages AT ferhatucar shipdetectordesignbasedondeepconvolutionalneuralnetworksforsatelliteimages AT denizkorkmaz shipdetectordesignbasedondeepconvolutionalneuralnetworksforsatelliteimages |