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