A Ship Detector Design Based on Deep Convolutional Neural Networks for Satellite Images
Yükleniyor...
Dosyalar
Tarih
2020
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Sakarya Üniversitesi
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Ship detection and classification systems from satellite images are challenging tasks with their requirements of feature extracting, advanced pre-processing, a variety of parameters obtained from satellites and other types of images, and analyzing of images. The dissimilarity of results, enhanced dataset requirement, the intricacy of the problem domain, general use of Synthetic Aperture Radar (SAR) images and problems on generalizability are some topics of the issues related to ship detection. In this study, we propose a Deep Convolutional Neural Network (DCNN) model for detecting the ships using the satellite images as inputs. Our model has acquired an adequate accuracy value by just using a pre-processed satellite image with a deep learning model built from scratch. The designed CNN model is constructed with a plain and easy to implement form in particular to the preferred satellite image set. Visual and graphical results show that the proposed model provides an efficient detection process with an accuracy of 99.60%.
Açıklama
Anahtar Kelimeler
deep convolutional neural networks, ship detection, remote sensing, satellite imagery
Kaynak
Sakarya University Journal of Science
WoS Q Değeri
Scopus Q Değeri
Cilt
24
Sayı
1
Künye
Ucar, F., & Korkmaz, D. (2020). A Ship Detector Design Based on Deep Convolutional Neural Networks for Satellite Images. Sakarya University Journal of Science, 24, 197-204.
