Ship Identification on Satellite Image Using Convolutional Neural Network and Random Forest

by Endang Anggiratih and Agfianto Eko Putra

Abstract

Ship identification on satellite imagery can be used for fisheries management, monitoring of smuggling activities, ship traffic services, and naval warfare. However, high-resolution satellite imagery also makes the segmentation of the ship difficult in the background, so that to handle it requires reliable features so that it can be identified adequately between large vessels, small vessels and not ships. The Convolution Neural Network (CNN) method, which has the advantage of being able to extract features automatically and produce reliable features that facilitate ship identification. This study combines CNN ZFNet architecture with the Random Forest method. The training was conducted with the aim of knowing the accuracy of the ZFNet layers to produce the best features, which are characterized by high accuracy, combined with the Random Forest method. Testing the combination of this method is done with two parameters, namely batch size and a number of trees. The test results identify large vessels with an accuracy of 87.5% and small vessels with an accuracy of not up to 50%.

(for more information please click https://doi.org/10.22146/ijccs.37461)

Tags: CNN, features extraction, Random Forest, ships identification, ZFNet