Comparison of CNN and SVM for Ship Detection in Satellite Imagery

Abstract

Satellites with optical sensors generate images of the Earth over relatively large areas. Optical satellite images provides unique insights into various markets, including agriculture, defense and intelligence, and energy. Ship detection using satellite images is very important because it can help manage marine traffic services, defense and intelligence, and fisheries management. In this study, optical satellite images are used for training models for detecting ship. Machine Learning (ML) algorithms such as deep learning and Support Vector Machine (SVM) have been applied to detect objects in previous studies. Convolution Neural Network (CNN)-based deep learning technology outperformed many algorithms that have existed to some extent[1]. CNN has proven to be able to outperform SVM to detect ships with an average training accuracy is 0,9912 or 99.12% and the validation accuracy is 0,9798 or97,89%. While SVM gets an accuracy of 0,9438 or 94,38%.