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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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Sakarya University
2020-02-01
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| Series: | Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi |
| Subjects: | |
| Online Access: | https://dergipark.org.tr/tr/download/article-file/967515 |
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| Summary: | 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. |
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| ISSN: | 2147-835X |