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

Year 2020, , 197 - 204, 01.02.2020

Ferhat Ucar , Deniz Korkmaz

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

Cited By: 2

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.

Keywords

deep convolutional neural networks (CNNs), ship target classification, remote sensing, satellite imagery

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