Technical gazette, Vol. 28 No. 4, 2021.
Original scientific paper
https://doi.org/10.17559/TV-20200522115821
Intelligent Automation System for Vessels Recognition: Comparison of SIFT and SURF Methods
Jelena Musulin*
orcid.org/0000-0002-5213-1550
; Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka
Ivan Lorencin
; Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka
Hrvoje Meštrić
; Catolic University of Croatia, Ilica 242, 10000 Zagreb
Zlatan Car
; Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka
Abstract
Nowadays, with the rise of drone and satellite technology, there is a possibility for its application in sea and coastal surveillance. An advantage of this type of application is the automated recognition of marine objects, among which the most important are vessels. This paper presents the principle of vessel recognition based on the extraction of satellite image features of the vessel and the application of a multilayer perceptron (MLP). Dataset used in this research contains the total of 2750 images, where 2112 images are used as training set while the remaining 638 images are used for testing purposes. The SIFT and SURF algorithms were used to extract image features, which were later used as the input vector for MLP.The best results are achieved if a model with four hidden layers is used. These layers are constructed with 32, 128, 32, 128 neurons with ReLU activation function, respectively. Regarding the application of feature extraction, it can be observed that better results are achieved if the SIFT algorithm is used. The ROC AUC value achieved with the combination of SIFT and MLP reaches 0.99.
Keywords
MLP; Satellite Images; SIFT; SURF; Vessels Classification
Hrčak ID:
260799
URI
Publication date:
22.7.2021.
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