A Ship Detector Design Based on Deep Convolutional Neural Networks for Satellite Images

Ferhat UCAR; Deniz KORKMAZ

doi:https://doi.org/10.16984/saufenbilder.587731

Article Details

A Ship Detector Design Based on Deep Convolutional Neural Networks for Satellite Images

Authors:

Ferhat UCAR | Deniz KORKMAZ

Main Topics:

Applied physics

Journal:

Sakarya University Journal of Science 2020 Vol.24 Issue 1, pp.197-204

Document Type:

Article

Publication Date:

1/2020

Document Language:

English

Document ID (Org No.):

2000556930

DOI:

https://doi.org/10.16984/saufenbilder.587731

Abstract:

 


Ship target
classification from satellite images is a challenging task with its
requirements of feature extracting, advanced pre-processing, a variety of
parameters obtained from satellites and other type of images, and analyzing of
images. The dissimilarity of results, enhanced dataset requirement, 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
target detection. In this study, we propose a deep convolutional neural network
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 input. Visual and graphical
results of features at various layers and deconvolutions are also demonstrated
for a better understanding of the basic process.


.

Article Details

A Ship Detector Design Based on Deep Convolutional Neural Networks for Satellite Images

Authors: Ferhat UCAR | Deniz KORKMAZ

Main Topics: Applied physics

Journal: Sakarya University Journal of Science 2020 Vol.24 Issue 1, pp.197-204

Document Type: Article

Publication Date: 1/2020

Document Language: English

Document ID (Org No.): 2000556930

DOI: https://doi.org/10.16984/saufenbilder.587731

Issues

Authors:

Ferhat UCAR | Deniz KORKMAZ

Main Topics:

Applied physics

Journal:

Sakarya University Journal of Science 2020 Vol.24 Issue 1, pp.197-204

Document Type:

Article

Publication Date:

1/2020

Document Language:

English

Document ID (Org No.):

2000556930

DOI:

https://doi.org/10.16984/saufenbilder.587731