Maritime trade and trasport occupy a pivotal position in the current era of globalization. Thus, monitoring ships at sea represents the starting point of this paper in which a novel approach to detect ships by wake has been proposed, based on Instance Segmentation deep learning architecture Mask R-CNN. In order to train and test this network, 766 wake chips cropped from 50 multispectral images acquired from Sentinel-2 satellites were observed. In particular, B2 (blue), B3 (green), B4 (red) and B8 (Infrared) bands were considered since they are all characterized by same resolution. The results proved that Mask R-CNN is capable to detect the vast majority of ship wakes with high confidence percentage in different configurations, i.e. slanted wakes, multiple wake scenarios or wakes in dark areas not related to their features.

First Results of Ship Wake Detection by Deep Learning Techniques in Multispectral Spaceborne Images / Esposito, C.; Del Prete, R.; Graziano, M. D.; Renga, A.. - 2022-:(2022), pp. 2167-2170. (Intervento presentato al convegno 2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022 tenutosi a Malaysia nel 2022) [10.1109/IGARSS46834.2022.9883511].

First Results of Ship Wake Detection by Deep Learning Techniques in Multispectral Spaceborne Images

Esposito C.;Del Prete R.;Graziano M. D.;Renga A.
2022

Abstract

Maritime trade and trasport occupy a pivotal position in the current era of globalization. Thus, monitoring ships at sea represents the starting point of this paper in which a novel approach to detect ships by wake has been proposed, based on Instance Segmentation deep learning architecture Mask R-CNN. In order to train and test this network, 766 wake chips cropped from 50 multispectral images acquired from Sentinel-2 satellites were observed. In particular, B2 (blue), B3 (green), B4 (red) and B8 (Infrared) bands were considered since they are all characterized by same resolution. The results proved that Mask R-CNN is capable to detect the vast majority of ship wakes with high confidence percentage in different configurations, i.e. slanted wakes, multiple wake scenarios or wakes in dark areas not related to their features.
2022
978-1-6654-2792-0
First Results of Ship Wake Detection by Deep Learning Techniques in Multispectral Spaceborne Images / Esposito, C.; Del Prete, R.; Graziano, M. D.; Renga, A.. - 2022-:(2022), pp. 2167-2170. (Intervento presentato al convegno 2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022 tenutosi a Malaysia nel 2022) [10.1109/IGARSS46834.2022.9883511].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/911407
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