Ship detection using optical satellite imagery

Given Singapore is a coastal state that connects the West and the East. Being one of the world’s busiest port, maritime security plays a vital role. A comprehensive maritime monitoring system should be developed so as to accommodate growing traffic demand. This project aims at automated extraction o...

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Bibliographic Details
Main Author: Hu, Jing
Other Authors: Lu Yilong
Format: Final Year Project (FYP)
Language:English
Published: 2016
Subjects:
Online Access:http://hdl.handle.net/10356/69325
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author Hu, Jing
author2 Lu Yilong
author_facet Lu Yilong
Hu, Jing
author_sort Hu, Jing
collection NTU
description Given Singapore is a coastal state that connects the West and the East. Being one of the world’s busiest port, maritime security plays a vital role. A comprehensive maritime monitoring system should be developed so as to accommodate growing traffic demand. This project aims at automated extraction of sea-going vessels using the optical imaging products of 21AT’s TripleSat Constellation. Traditionally, synthetic-aperture radar (SAR) satellite imagery is used due to its being “All Weather, All Day” and its prominent target reflections that make ship detection fast and robust. However, optical satellite imagery has its edge over the SAR counterpart in the choices of bands, resolutions and revisit rates, let alone the costs. The implementation of this project leverage on Matlab with image processing toolbox. There are two filtering conditions involved in this Matlab program, which are length-beam ratio and roundness. By using these two constrains, the program can successfully detect sea-going ships among all those false alarms like small island, wakes and cloud. However, due to the limitations of this research, other factors are not considered. For future research, it is highly recommended to have more filtering criteria and apply machine learning in the algorithm.
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spelling ntu-10356/693252023-07-07T17:53:22Z Ship detection using optical satellite imagery Hu, Jing Lu Yilong School of Electrical and Electronic Engineering 21AT’s TripleSat Constellation DRNTU::Engineering Given Singapore is a coastal state that connects the West and the East. Being one of the world’s busiest port, maritime security plays a vital role. A comprehensive maritime monitoring system should be developed so as to accommodate growing traffic demand. This project aims at automated extraction of sea-going vessels using the optical imaging products of 21AT’s TripleSat Constellation. Traditionally, synthetic-aperture radar (SAR) satellite imagery is used due to its being “All Weather, All Day” and its prominent target reflections that make ship detection fast and robust. However, optical satellite imagery has its edge over the SAR counterpart in the choices of bands, resolutions and revisit rates, let alone the costs. The implementation of this project leverage on Matlab with image processing toolbox. There are two filtering conditions involved in this Matlab program, which are length-beam ratio and roundness. By using these two constrains, the program can successfully detect sea-going ships among all those false alarms like small island, wakes and cloud. However, due to the limitations of this research, other factors are not considered. For future research, it is highly recommended to have more filtering criteria and apply machine learning in the algorithm. Bachelor of Engineering 2016-12-13T08:30:23Z 2016-12-13T08:30:23Z 2016 Final Year Project (FYP) http://hdl.handle.net/10356/69325 en Nanyang Technological University 53 p. application/pdf
spellingShingle DRNTU::Engineering
Hu, Jing
Ship detection using optical satellite imagery
title Ship detection using optical satellite imagery
title_full Ship detection using optical satellite imagery
title_fullStr Ship detection using optical satellite imagery
title_full_unstemmed Ship detection using optical satellite imagery
title_short Ship detection using optical satellite imagery
title_sort ship detection using optical satellite imagery
topic DRNTU::Engineering
url http://hdl.handle.net/10356/69325
work_keys_str_mv AT hujing shipdetectionusingopticalsatelliteimagery