Ship Classification Using Layover in Sentinel-1 Images

Al Adil Al Hinai; Raffaella Guida

doi:10.1109/IGARSS52108.2023.10282911

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Conference proceeding

Ship Classification Using Layover in Sentinel-1 Images

Al Adil Al Hinai and Raffaella Guida

IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, pp.7495-7498

IEEE International Geoscience and Remote Sensing Symposium (Pasadena, CA, USA, 16/07/2023 - 21/07/2023)

16/07/2023

DOI: https://doi.org/10.1109/IGARSS52108.2023.10282911

Abstract

Classification algorithms

European Space Agency

Image edge detection

layover

Oils

Radar polarimetry

Satellite constellations

Seaports

Sentinel-1

ship classification

Synthetic aperture radar (SAR)

In this paper, a novel algorithm for ship classification in Sentinel-1 synthetic aperture radar (SAR) images is presented. The algorithm utilises layover as the main classification feature, which is based on the different relative heights of superstructures in oil tankers, container ships, geared and gearless bulk carriers. The algorithm has been tested using 20 ship samples from Sentinel-1 stripmap images over the port of Santos, divided equally amongst the four ship classes. An overall classification accuracy of 75% has been achieved.

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Title: Ship Classification Using Layover in Sentinel-1 Images
Creators: Al Adil Al Hinai - University of Surrey
Raffaella Guida - University of Surrey
Publication Details: IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, pp.7495-7498
Conference: IEEE International Geoscience and Remote Sensing Symposium (Pasadena, CA, USA, 16/07/2023 - 21/07/2023)
Publisher: IEEE
Date published: 16/07/2023
Identifiers: 99822739202346
Academic Unit: School of Computer Science and Electronic Engineering; Institute for Sustainability
Language: English
Resource Type: Conference proceeding

Ship Classification Using Layover in Sentinel-1 Images

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